onsuccess: #161: 1: [TCWG CI] https://ci.linaro.org/job/tcwg_bmk-code_speed-spec2k6--gnu-arm-master-O3-build/161/

Results :
  | # reset_artifacts:
  | -10
  | # build_abe binutils:
  | -9
  | # build_abe stage1 -- --set gcc_override_configure=--with-mode=arm --set gcc_override_configure=--disable-libsanitizer:
  | -8
  | # build_abe linux:
  | -7
  | # build_abe glibc:
  | -6
  | # build_abe stage2 -- --set gcc_override_configure=--with-mode=arm --set gcc_override_configure=--disable-libsanitizer:
  | -5
  | # benchmark -- -O3_marm:
  | 1

check_regression status : 0

6 files changed, 3795 insertions, 2452 deletions

diff --git a/notify/any.skipped b/notify/any.skipped
deleted file mode 100644
index cccc3235..00000000
--- a/notify/any.skipped
+++ /dev/null
@@ -1 +0,0 @@
-458.sjeng,[.] gen,slowed down by 16% - 458.sjeng:[.] gen,slowed down by 16% - 458.sjeng:[.] gen - from 1116 to 1290 perf samples

diff --git a/notify/jira/comment-template.txt b/notify/jira/comment-template.txt
index 047c162d..1f6945f3 100644
--- a/notify/jira/comment-template.txt
+++ b/notify/jira/comment-template.txt
@@ -1,3 +1,3 @@
 [GNU-689]
 No change
-Details: https://ci.linaro.org/job/tcwg_bmk-code_speed-spec2k6--gnu-arm-master-O3-build/160/artifact/artifacts/notify/mail-body.txt/*view*/
+Details: https://ci.linaro.org/job/tcwg_bmk-code_speed-spec2k6--gnu-arm-master-O3-build/161/artifact/artifacts/notify/mail-body.txt/*view*/
diff --git a/notify/lnt_report.json b/notify/lnt_report.json
new file mode 100644
index 00000000..9ec4852c
--- /dev/null
+++ b/notify/lnt_report.json
@@ -0,0 +1,463 @@
+{
+    "Machine": {
+        "Info": {},
+        "Name": "tcwg_bmk-code_speed-spec2k6_gnu-arm-master-O3"
+    },
+    "Run": {
+        "Info": {
+            "__report_version__": "1",
+            "run_order": "basepoints/gcc-14-05525-ge36d5f424a0",
+            "tag": "tcwg_bmk"
+        },
+        "Start Time": "2023-11-16 14:18:08"
+    },
+    "Tests": [
+        {
+            "Data": [
+                1487149
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.445.gobmk.code_size"
+        }
+	   ,
+        {
+            "Data": [
+                941454
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.453.povray.code_size"
+        }
+	   ,
+        {
+            "Data": [
+                3142431
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.447.dealII.code_size"
+        }
+	   ,
+        {
+            "Data": [
+                410501
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.450.soplex.code_size"
+        }
+	   ,
+        {
+            "Data": [
+                1752678
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.454.calculix.code_size"
+        }
+	   ,
+        {
+            "Data": [
+                299825
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.456.hmmer.code_size"
+        }
+	   ,
+        {
+            "Data": [
+                158220
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.458.sjeng.code_size"
+        }
+	   ,
+        {
+            "Data": [
+                405541
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.459.GemsFDTD.code_size"
+        }
+	   ,
+        {
+            "Data": [
+                197607
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.482.sphinx3.code_size"
+        }
+	   ,
+        {
+            "Data": [
+                7092734
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.481.wrf.code_size"
+        }
+	   ,
+        {
+            "Data": [
+                933589
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.435.gromacs.code_size"
+        }
+	   ,
+        {
+            "Data": [
+                236626
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.444.namd.code_size"
+        }
+	   ,
+        {
+            "Data": [
+                768263
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.436.cactusADM.code_size"
+        }
+	   ,
+        {
+            "Data": [
+                113471
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.437.leslie3d.code_size"
+        }
+	   ,
+        {
+            "Data": [
+                38052
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.473.astar.code_size"
+        }
+	   ,
+        {
+            "Data": [
+                12294
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.470.lbm.code_size"
+        }
+	   ,
+        {
+            "Data": [
+                555086
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.471.omnetpp.code_size"
+        }
+	   ,
+        {
+            "Data": [
+                1186073
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.400.perlbench.code_size"
+        }
+	   ,
+        {
+            "Data": [
+                3178457
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.403.gcc.code_size"
+        }
+	   ,
+        {
+            "Data": [
+                24978
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.410.bwaves.code_size"
+        }
+	   ,
+        {
+            "Data": [
+                75080
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.401.bzip2.code_size"
+        }
+	   ,
+        {
+            "Data": [
+                795252
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.464.h264ref.code_size"
+        }
+	   ,
+        {
+            "Data": [
+                35103
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.462.libquantum.code_size"
+        }
+	   ,
+        {
+            "Data": [
+                4346893
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.465.tonto.code_size"
+        }
+	   ,
+        {
+            "Data": [
+                9584542
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.416.gamess.code_size"
+        }
+	   ,
+        {
+            "Data": [
+                336938
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.434.zeusmp.code_size"
+        }
+	   ,
+        {
+            "Data": [
+                131334
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.433.milc.code_size"
+        }
+	   ,
+        {
+            "Data": [
+                10675
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.429.mcf.code_size"
+        }
+	   ,
+        {
+            "Data": [
+                12645
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.462.libquantum.exec"
+        }
+	   ,
+        {
+            "Data": [
+                9628
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.464.h264ref.exec"
+        }
+	   ,
+        {
+            "Data": [
+                6606
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.465.tonto.exec"
+        }
+	   ,
+        {
+            "Data": [
+                17938
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.454.calculix.exec"
+        }
+	   ,
+        {
+            "Data": [
+                7742
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.456.hmmer.exec"
+        }
+	   ,
+        {
+            "Data": [
+                12896
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.458.sjeng.exec"
+        }
+	   ,
+        {
+            "Data": [
+                13254
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.459.GemsFDTD.exec"
+        }
+	   ,
+        {
+            "Data": [
+                13503
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.470.lbm.exec"
+        }
+	   ,
+        {
+            "Data": [
+                6331
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.471.omnetpp.exec"
+        }
+	   ,
+        {
+            "Data": [
+                8831
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.473.astar.exec"
+        }
+	   ,
+        {
+            "Data": [
+                10322
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.445.gobmk.exec"
+        }
+	   ,
+        {
+            "Data": [
+                5571
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.447.dealII.exec"
+        }
+	   ,
+        {
+            "Data": [
+                7664
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.450.soplex.exec"
+        }
+	   ,
+        {
+            "Data": [
+                4616
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.453.povray.exec"
+        }
+	   ,
+        {
+            "Data": [
+                9843
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.435.gromacs.exec"
+        }
+	   ,
+        {
+            "Data": [
+                26667
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.436.cactusADM.exec"
+        }
+	   ,
+        {
+            "Data": [
+                14322
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.437.leslie3d.exec"
+        }
+	   ,
+        {
+            "Data": [
+                9961
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.444.namd.exec"
+        }
+	   ,
+        {
+            "Data": [
+                21908
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.416.gamess.exec"
+        }
+	   ,
+        {
+            "Data": [
+                10802
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.429.mcf.exec"
+        }
+	   ,
+        {
+            "Data": [
+                10683
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.433.milc.exec"
+        }
+	   ,
+        {
+            "Data": [
+                12352
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.434.zeusmp.exec"
+        }
+	   ,
+        {
+            "Data": [
+                11437
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.481.wrf.exec"
+        }
+	   ,
+        {
+            "Data": [
+                23946
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.482.sphinx3.exec"
+        }
+	   ,
+        {
+            "Data": [
+                8020
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.400.perlbench.exec"
+        }
+	   ,
+        {
+            "Data": [
+                11368
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.401.bzip2.exec"
+        }
+	   ,
+        {
+            "Data": [
+                6267
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.403.gcc.exec"
+        }
+	   ,
+        {
+            "Data": [
+                12880
+            ],
+            "Info": {},
+            "Name": "tcwg_bmk.410.bwaves.exec"
+        }
+    ]
+}
diff --git a/notify/mail-body.txt b/notify/mail-body.txt
index 80bbb328..6350191a 100644
--- a/notify/mail-body.txt
+++ b/notify/mail-body.txt
@@ -2,19 +2,25 @@ Dear contributor, our automatic CI has detected problems related to your patch(e
 
 In CI config tcwg_bmk-code_speed-spec2k6/gnu-arm-master-O3 after:
 
-  | 85 commits in binutils,gcc
-  | 6b682bbf86f [gdb/tui] Fix Wmaybe-uninitialized in tui_find_disassembly_address
-  | aba9fa5f4be [gdb/tui] Make assert in tui_find_disassembly_address more strict
-  | e5da53e26f4 Remove declaration of re_comp
-  | df3926bb636 Automatic date update in version.in
-  | 328e0159543 Automatic date update in version.in
-  | ... and 9 more commits in binutils
-  | 2794d510b97 Support vec_set/vec_extract/vec_init for V4HF/V2HF.
-  | b51bfee1bee ARC: Improved DImode rotates and right shifts by one bit.
-  | e9d59a2a5a8 ARC: Provide a TARGET_FOLD_BUILTIN target hook.
-  | 0a140730c97 i386: Improve reg pressure of double word right shift then truncate.
-  | 56caf0b435c i386: Remove j constraint letter from list of unused letters
-  | ... and 66 more commits in gcc
+  | 226 commits in binutils,gcc,glibc
+  | 8d081332318 gdb/NEWS: merge two 'New commands' sections
+  | c441a361287 Fix emit-relocs for aarch64 gold
+  | 27c22a4c76f sim: mips: Change E_MIPS_* to EF_MIPS_*
+  | 66cf42940aa gdb: mips: Change E_MIPS_* to EF_MIPS_*
+  | 5391e967b03 Automatic date update in version.in
+  | ... and 69 more commits in binutils
+  | e36d5f424a0 Fix ICE of unrecognizable insn.
+  | c7f6537db94 libstdc++: Implement std::out_ptr and std::inout_ptr for C++23 [PR111667]
+  | 7ffa63df8f5 libstdc++: Only declare feature test macros in standard headers
+  | f4ab68469cc libstdc++: Test for feature test macros more accurately
+  | e469f9003dd libstdc++: Use 202100L as feature test check for C++23
+  | ... and 132 more commits in gcc
+  | b7f8b6b64b x86: Fix unchecked AVX512-VBMI2 usage in strrchr-evex-base.S
+  | 0575073dda posix: Check pidfd_spawn with tst-spawn7-pid
+  | 578190b7e4 sparc: Fix broken memset for sparc32 [BZ #31068]
+  | baea60b33e y2038: Fix support for 64-bit time on legacy ABIs
+  | 323f367cc4 hurd: Fix spawni returning allocation errors.
+  | ... and 10 more commits in glibc
 
 No change
 
@@ -34,6 +40,6 @@ This benchmarking CI is work-in-progress, and we welcome feedback and suggestion
 -----------------8<--------------------------8<--------------------------8<--------------------------
 The information below can be used to reproduce a debug environment:
 
-Current build   : https://ci.linaro.org/job/tcwg_bmk-code_speed-spec2k6--gnu-arm-master-O3-build/160/artifact/artifacts
-Reference build : https://ci.linaro.org/job/tcwg_bmk-code_speed-spec2k6--gnu-arm-master-O3-build/159/artifact/artifacts
+Current build   : https://ci.linaro.org/job/tcwg_bmk-code_speed-spec2k6--gnu-arm-master-O3-build/161/artifact/artifacts
+Reference build : https://ci.linaro.org/job/tcwg_bmk-code_speed-spec2k6--gnu-arm-master-O3-build/160/artifact/artifacts
 
diff --git a/notify/mail-subject.txt b/notify/mail-subject.txt
index adef9833..c457e5f5 100644
--- a/notify/mail-subject.txt
+++ b/notify/mail-subject.txt
@@ -1 +1 @@
-[Linaro-TCWG-CI] 85 commits in binutils,gcc: No change on arm O3
+[Linaro-TCWG-CI] 226 commits in binutils,gcc,glibc: No change on arm O3
diff --git a/notify/output-bmk-results.log b/notify/output-bmk-results.log
index cc24340d..818dda48 100644
--- a/notify/output-bmk-results.log
+++ b/notify/output-bmk-results.log
@@ -1,106 +1,1020 @@
  --- modulename: output-bmk-results, funcname: <module>
 <string>(1):  --- modulename: output-bmk-results, funcname: main
-output-bmk-results.py(287):     results_csv = sys.argv[1]
-output-bmk-results.py(288):     variability_file = sys.argv[2]
-output-bmk-results.py(289):     run_step_artifacts_dir = sys.argv[3]
-output-bmk-results.py(290):     metric = sys.argv[4]
-output-bmk-results.py(291):     mode = sys.argv[5]
-output-bmk-results.py(292):     details = sys.argv[6]
-output-bmk-results.py(294):     merged_df = read_results_csv(results_csv)
+output-bmk-results.py(322):     results_csv = sys.argv[1]
+output-bmk-results.py(323):     variability_file = sys.argv[2]
+output-bmk-results.py(324):     run_step_artifacts_dir = sys.argv[3]
+output-bmk-results.py(325):     metric = sys.argv[4]
+output-bmk-results.py(326):     mode = sys.argv[5]
+output-bmk-results.py(327):     details = sys.argv[6]
+output-bmk-results.py(329):     merged_df = read_results_csv(results_csv)
  --- modulename: output-bmk-results, funcname: read_results_csv
-output-bmk-results.py(277):     df = pd.read_csv(results_csv)
-output-bmk-results.py(278):     df = df.fillna(-1)
-output-bmk-results.py(280):     for metric in get_comparable_metrics(df):
+output-bmk-results.py(312):     df = pd.read_csv(results_csv)
+output-bmk-results.py(313):     df = df.fillna(-1)
+output-bmk-results.py(315):     for metric in get_comparable_metrics(df):
  --- modulename: output-bmk-results, funcname: get_comparable_metrics
-output-bmk-results.py(198):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
+output-bmk-results.py(206):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
  --- modulename: output-bmk-results, funcname: <genexpr>
-output-bmk-results.py(198):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
+output-bmk-results.py(206):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
  --- modulename: output-bmk-results, funcname: <genexpr>
-output-bmk-results.py(198):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
+output-bmk-results.py(206):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
  --- modulename: output-bmk-results, funcname: <genexpr>
-output-bmk-results.py(198):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
+output-bmk-results.py(206):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
  --- modulename: output-bmk-results, funcname: <genexpr>
-output-bmk-results.py(198):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
+output-bmk-results.py(206):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
  --- modulename: output-bmk-results, funcname: <genexpr>
-output-bmk-results.py(198):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
+output-bmk-results.py(206):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
  --- modulename: output-bmk-results, funcname: <genexpr>
-output-bmk-results.py(198):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
-output-bmk-results.py(198):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
-output-bmk-results.py(198):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
-output-bmk-results.py(198):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
-output-bmk-results.py(198):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
-output-bmk-results.py(198):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
-output-bmk-results.py(198):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
-output-bmk-results.py(198):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
-output-bmk-results.py(198):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
-output-bmk-results.py(198):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
-output-bmk-results.py(198):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
-output-bmk-results.py(199):            & metric_utils.comparable_metrics
-output-bmk-results.py(198):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
-output-bmk-results.py(281):         df["rel_" + metric] = df["rel_" + metric].astype("int")
-output-bmk-results.py(282):         df[metric + "_x"] = df[metric + "_x"].astype("int")
-output-bmk-results.py(283):         df[metric + "_y"] = df[metric + "_y"].astype("int")
-output-bmk-results.py(280):     for metric in get_comparable_metrics(df):
-output-bmk-results.py(281):         df["rel_" + metric] = df["rel_" + metric].astype("int")
-output-bmk-results.py(282):         df[metric + "_x"] = df[metric + "_x"].astype("int")
-output-bmk-results.py(283):         df[metric + "_y"] = df[metric + "_y"].astype("int")
-output-bmk-results.py(280):     for metric in get_comparable_metrics(df):
-output-bmk-results.py(281):         df["rel_" + metric] = df["rel_" + metric].astype("int")
-output-bmk-results.py(282):         df[metric + "_x"] = df[metric + "_x"].astype("int")
-output-bmk-results.py(283):         df[metric + "_y"] = df[metric + "_y"].astype("int")
-output-bmk-results.py(280):     for metric in get_comparable_metrics(df):
-output-bmk-results.py(281):         df["rel_" + metric] = df["rel_" + metric].astype("int")
-output-bmk-results.py(282):         df[metric + "_x"] = df[metric + "_x"].astype("int")
-output-bmk-results.py(283):         df[metric + "_y"] = df[metric + "_y"].astype("int")
-output-bmk-results.py(280):     for metric in get_comparable_metrics(df):
-output-bmk-results.py(284):     return df
-output-bmk-results.py(295):     read_specific_variability_file(variability_file)
+output-bmk-results.py(206):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
+ --- modulename: output-bmk-results, funcname: <genexpr>
+output-bmk-results.py(206):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
+output-bmk-results.py(206):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
+output-bmk-results.py(206):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
+output-bmk-results.py(206):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
+output-bmk-results.py(206):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
+output-bmk-results.py(206):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
+output-bmk-results.py(206):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
+output-bmk-results.py(206):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
+output-bmk-results.py(206):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
+output-bmk-results.py(206):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
+output-bmk-results.py(206):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
+output-bmk-results.py(206):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
+output-bmk-results.py(206):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
+output-bmk-results.py(207):            & metric_utils.comparable_metrics
+output-bmk-results.py(206):     return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \
+output-bmk-results.py(316):         df["rel_" + metric] = df["rel_" + metric].astype("int")
+output-bmk-results.py(317):         df[metric + "_x"] = df[metric + "_x"].astype("int")
+output-bmk-results.py(318):         df[metric + "_y"] = df[metric + "_y"].astype("int")
+output-bmk-results.py(315):     for metric in get_comparable_metrics(df):
+output-bmk-results.py(316):         df["rel_" + metric] = df["rel_" + metric].astype("int")
+output-bmk-results.py(317):         df[metric + "_x"] = df[metric + "_x"].astype("int")
+output-bmk-results.py(318):         df[metric + "_y"] = df[metric + "_y"].astype("int")
+output-bmk-results.py(315):     for metric in get_comparable_metrics(df):
+output-bmk-results.py(316):         df["rel_" + metric] = df["rel_" + metric].astype("int")
+output-bmk-results.py(317):         df[metric + "_x"] = df[metric + "_x"].astype("int")
+output-bmk-results.py(318):         df[metric + "_y"] = df[metric + "_y"].astype("int")
+output-bmk-results.py(315):     for metric in get_comparable_metrics(df):
+output-bmk-results.py(316):         df["rel_" + metric] = df["rel_" + metric].astype("int")
+output-bmk-results.py(317):         df[metric + "_x"] = df[metric + "_x"].astype("int")
+output-bmk-results.py(318):         df[metric + "_y"] = df[metric + "_y"].astype("int")
+output-bmk-results.py(315):     for metric in get_comparable_metrics(df):
+output-bmk-results.py(319):     return df
+output-bmk-results.py(330):     read_specific_variability_file(variability_file)
  --- modulename: output-bmk-results, funcname: read_specific_variability_file
 output-bmk-results.py(51):     if not os.path.exists(bmk_specific_filename):
 output-bmk-results.py(53):     specific_variability = pd.read_csv(bmk_specific_filename, index_col=False)
-output-bmk-results.py(296):     output_bmk_results(merged_df, run_step_artifacts_dir, metric, mode, details)
+output-bmk-results.py(331):     output_bmk_results(merged_df, run_step_artifacts_dir, metric, mode, details)
  --- modulename: output-bmk-results, funcname: output_bmk_results
-output-bmk-results.py(248):     f_regr = Outfile("{0}/results.regressions".format(run_step_artifacts), "w")
+output-bmk-results.py(278):     f_regr = Outfile("{0}/results.regressions".format(run_step_artifacts), "w")
  --- modulename: output-bmk-results, funcname: __init__
 output-bmk-results.py(19):     self.filename=filename
 output-bmk-results.py(20):     self.predicate=predicate
 output-bmk-results.py(21):     if predicate:
 output-bmk-results.py(22):        self.outf = open(filename, mode)
 output-bmk-results.py(23):        self.csvwriter = csv.writer(self.outf)
-output-bmk-results.py(249):     f_ebp  = Outfile("{0}/extra-bisect-params".format(run_step_artifacts), "w")
+output-bmk-results.py(279):     f_ebp  = Outfile("{0}/extra-bisect-params".format(run_step_artifacts), "w")
  --- modulename: output-bmk-results, funcname: __init__
 output-bmk-results.py(19):     self.filename=filename
 output-bmk-results.py(20):     self.predicate=predicate
 output-bmk-results.py(21):     if predicate:
 output-bmk-results.py(22):        self.outf = open(filename, mode)
 output-bmk-results.py(23):        self.csvwriter = csv.writer(self.outf)
-output-bmk-results.py(250):     f_skip = Outfile("{0}/any.skipped".format(run_step_artifacts), "w", predicate=(details=="verbose"))
+output-bmk-results.py(280):     f_skip = Outfile("{0}/any.skipped".format(run_step_artifacts), "w", predicate=(details=="verbose"))
  --- modulename: output-bmk-results, funcname: __init__
 output-bmk-results.py(19):     self.filename=filename
 output-bmk-results.py(20):     self.predicate=predicate
 output-bmk-results.py(21):     if predicate:
 output-bmk-results.py(22):        self.outf = open(filename, mode)
 output-bmk-results.py(23):        self.csvwriter = csv.writer(self.outf)
-output-bmk-results.py(252):     f_ebp.write("extra_build_params=")
+output-bmk-results.py(282):     f_ebp.write("extra_build_params=")
  --- modulename: output-bmk-results, funcname: write
 output-bmk-results.py(36):     if not self.predicate or not self.outf:
 output-bmk-results.py(38):     self.outf.write(string)
-output-bmk-results.py(256):     df = merged_df[merged_df["benchmark"] != "Mean"]
-output-bmk-results.py(259):     exe_df = df[df["symbol"].str.endswith("_base.default")]
-output-bmk-results.py(260):     sym_df = df[~df["symbol"].str.endswith("_base.default")]
-output-bmk-results.py(262):     output_bmk_results_1(exe_df, "exe", "regression", f_regr, f_skip, f_ebp, run_step_artifacts, metric, mode, details)
+output-bmk-results.py(286):     df = merged_df[merged_df["benchmark"] != "Mean"]
+output-bmk-results.py(289):     exe_df = df[df["symbol"].str.endswith("_base.default")]
+output-bmk-results.py(290):     sym_df = df[~df["symbol"].str.endswith("_base.default")]
+output-bmk-results.py(293):     output_bmk_results_status(exe_df, "regression", f_regr, f_ebp, run_step_artifacts, details)
+ --- modulename: output-bmk-results, funcname: output_bmk_results_status
+output-bmk-results.py(256):     f_out = Outfile("{0}/status.{1}".format(run_step_artifacts, change_kind), "w", predicate=(details=="verbose"))
+ --- modulename: output-bmk-results, funcname: __init__
+output-bmk-results.py(19):     self.filename=filename
+output-bmk-results.py(20):     self.predicate=predicate
+output-bmk-results.py(21):     if predicate:
+output-bmk-results.py(22):        self.outf = open(filename, mode)
+output-bmk-results.py(23):        self.csvwriter = csv.writer(self.outf)
+output-bmk-results.py(258):     print(results_df)
+          benchmark  ...         status_y
+0     400.perlbench  ...          success
+3         401.bzip2  ...          success
+9           403.gcc  ...          success
+13       410.bwaves  ...          success
+16       416.gamess  ...          success
+21          429.mcf  ...          success
+24         433.milc  ...          success
+30       434.zeusmp  ...          success
+33      435.gromacs  ...          success
+36    436.cactusADM  ...          success
+38     437.leslie3d  ...          success
+44         444.namd  ...          success
+51        445.gobmk  ...          success
+54       447.dealII  ...          success
+60       450.soplex  ...          success
+64       453.povray  ...          success
+69     454.calculix  ...          success
+72        456.hmmer  ...          success
+74        458.sjeng  ...          success
+78     459.GemsFDTD  ...          success
+84   462.libquantum  ...          success
+88      464.h264ref  ...          success
+93        465.tonto  ...          success
+99          470.lbm  ...          success
+101     471.omnetpp  ...          success
+105       473.astar  ...          success
+109         481.wrf  ...          success
+115     482.sphinx3  ...          success
+118   483.xalancbmk  ...  failed-to-build
+
+[29 rows x 20 columns]
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(275):     f_out.close()
+ --- modulename: output-bmk-results, funcname: close
+output-bmk-results.py(29):     if not self.outf:
+output-bmk-results.py(31):     self.outf.close()
+output-bmk-results.py(32):     if os.stat(self.filename).st_size == 0:
+output-bmk-results.py(33):        os.remove(self.filename)
+output-bmk-results.py(294):     output_bmk_results_status(exe_df, "improvement", None,  None,  run_step_artifacts, details)
+ --- modulename: output-bmk-results, funcname: output_bmk_results_status
+output-bmk-results.py(256):     f_out = Outfile("{0}/status.{1}".format(run_step_artifacts, change_kind), "w", predicate=(details=="verbose"))
+ --- modulename: output-bmk-results, funcname: __init__
+output-bmk-results.py(19):     self.filename=filename
+output-bmk-results.py(20):     self.predicate=predicate
+output-bmk-results.py(21):     if predicate:
+output-bmk-results.py(22):        self.outf = open(filename, mode)
+output-bmk-results.py(23):        self.csvwriter = csv.writer(self.outf)
+output-bmk-results.py(258):     print(results_df)
+          benchmark  ...         status_y
+0     400.perlbench  ...          success
+3         401.bzip2  ...          success
+9           403.gcc  ...          success
+13       410.bwaves  ...          success
+16       416.gamess  ...          success
+21          429.mcf  ...          success
+24         433.milc  ...          success
+30       434.zeusmp  ...          success
+33      435.gromacs  ...          success
+36    436.cactusADM  ...          success
+38     437.leslie3d  ...          success
+44         444.namd  ...          success
+51        445.gobmk  ...          success
+54       447.dealII  ...          success
+60       450.soplex  ...          success
+64       453.povray  ...          success
+69     454.calculix  ...          success
+72        456.hmmer  ...          success
+74        458.sjeng  ...          success
+78     459.GemsFDTD  ...          success
+84   462.libquantum  ...          success
+88      464.h264ref  ...          success
+93        465.tonto  ...          success
+99          470.lbm  ...          success
+101     471.omnetpp  ...          success
+105       473.astar  ...          success
+109         481.wrf  ...          success
+115     482.sphinx3  ...          success
+118   483.xalancbmk  ...  failed-to-build
+
+[29 rows x 20 columns]
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(263):         classif, short_diag = get_status_diag(row)
+ --- modulename: output-bmk-results, funcname: get_status_diag
+output-bmk-results.py(113):     bmk = row["benchmark"]
+output-bmk-results.py(115):     short_diag=""
+output-bmk-results.py(116):     classif=""
+output-bmk-results.py(118):     if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
+output-bmk-results.py(121):     elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(124):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
+output-bmk-results.py(127):     elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
+output-bmk-results.py(130):     elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
+output-bmk-results.py(134):     return classif, short_diag
+output-bmk-results.py(265):         if classif != change_kind:
+output-bmk-results.py(266):             continue;
+output-bmk-results.py(261):     for index, row in results_df.iterrows():
+output-bmk-results.py(275):     f_out.close()
+ --- modulename: output-bmk-results, funcname: close
+output-bmk-results.py(29):     if not self.outf:
+output-bmk-results.py(31):     self.outf.close()
+output-bmk-results.py(32):     if os.stat(self.filename).st_size == 0:
+output-bmk-results.py(33):        os.remove(self.filename)
+output-bmk-results.py(297):     output_bmk_results_1(exe_df, "exe", "regression", f_regr, f_skip, f_ebp, run_step_artifacts, metric, mode, details)
  --- modulename: output-bmk-results, funcname: output_bmk_results_1
-output-bmk-results.py(210):     f_out = Outfile("{0}/{1}.{2}".format(run_step_artifacts, sym_type, change_kind), "w", predicate=(details=="verbose"))
+output-bmk-results.py(218):     f_out = Outfile("{0}/{1}.{2}".format(run_step_artifacts, sym_type, change_kind), "w", predicate=(details=="verbose"))
  --- modulename: output-bmk-results, funcname: __init__
 output-bmk-results.py(19):     self.filename=filename
 output-bmk-results.py(20):     self.predicate=predicate
 output-bmk-results.py(21):     if predicate:
 output-bmk-results.py(22):        self.outf = open(filename, mode)
 output-bmk-results.py(23):        self.csvwriter = csv.writer(self.outf)
-output-bmk-results.py(212):     rel_metric = "rel_" + metric
-output-bmk-results.py(213):     out_df = results_df[results_df[rel_metric] != -1]
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(220):     rel_metric = "rel_" + metric
+output-bmk-results.py(221):     out_df = results_df[results_df[rel_metric] != -1]
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -117,17 +1031,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking exe.regression : 400.perlbench,perlbench_base.default : sample=0% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -144,17 +1058,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking exe.regression : 401.bzip2,bzip2_base.default : sample=2% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking exe.regression : 401.bzip2,bzip2_base.default : sample=1% (threshold=3%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -171,17 +1085,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking exe.regression : 403.gcc,gcc_base.default : sample=0% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -198,17 +1112,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking exe.regression : 410.bwaves,bwaves_base.default : sample=0% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -225,17 +1139,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking exe.regression : 416.gamess,gamess_base.default : sample=1% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking exe.regression : 416.gamess,gamess_base.default : sample=0% (threshold=3%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -252,17 +1166,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking exe.regression : 429.mcf,mcf_base.default : sample=0% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -279,17 +1193,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking exe.regression : 433.milc,milc_base.default : sample=0% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -306,17 +1220,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking exe.regression : 434.zeusmp,zeusmp_base.default : sample=0% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -333,17 +1247,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking exe.regression : 435.gromacs,gromacs_base.default : sample=0% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -360,17 +1274,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking exe.regression : 436.cactusADM,cactusADM_base.default : sample=0% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -387,17 +1301,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking exe.regression : 437.leslie3d,leslie3d_base.default : sample=1% (threshold=10.44%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking exe.regression : 437.leslie3d,leslie3d_base.default : sample=0% (threshold=10.5%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -414,17 +1328,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking exe.regression : 444.namd,namd_base.default : sample=0% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -441,17 +1355,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking exe.regression : 445.gobmk,gobmk_base.default : sample=0% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -468,17 +1382,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking exe.regression : 447.dealII,dealII_base.default : sample=0% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -495,17 +1409,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking exe.regression : 450.soplex,soplex_base.default : sample=0% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -522,17 +1436,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking exe.regression : 453.povray,povray_base.default : sample=0% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -549,17 +1463,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking exe.regression : 454.calculix,calculix_base.default : sample=0% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -576,17 +1490,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking exe.regression : 456.hmmer,hmmer_base.default : sample=-2% (threshold=3.96%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking exe.regression : 456.hmmer,hmmer_base.default : sample=2% (threshold=3.99%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -603,17 +1517,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking exe.regression : 458.sjeng,sjeng_base.default : sample=0% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -630,17 +1544,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking exe.regression : 459.GemsFDTD,GemsFDTD_base.default : sample=-1% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking exe.regression : 459.GemsFDTD,GemsFDTD_base.default : sample=0% (threshold=3%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -657,17 +1571,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking exe.regression : 462.libquantum,libquantum_base.default : sample=1% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking exe.regression : 462.libquantum,libquantum_base.default : sample=-1% (threshold=3%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -684,17 +1598,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking exe.regression : 464.h264ref,h264ref_base.default : sample=-1% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking exe.regression : 464.h264ref,h264ref_base.default : sample=1% (threshold=3%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -711,17 +1625,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking exe.regression : 465.tonto,tonto_base.default : sample=0% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -738,17 +1652,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking exe.regression : 470.lbm,lbm_base.default : sample=0% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking exe.regression : 470.lbm,lbm_base.default : sample=-1% (threshold=3%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -765,17 +1679,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking exe.regression : 471.omnetpp,omnetpp_base.default : sample=0% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -792,17 +1706,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking exe.regression : 473.astar,astar_base.default : sample=0% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -819,17 +1733,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking exe.regression : 481.wrf,wrf_base.default : sample=1% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -846,62 +1760,35 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking exe.regression : 482.sphinx3,sphinx_livepretend_base.default : sample=0% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking exe.regression : 482.sphinx3,sphinx_livepretend_base.default : sample=-2% (threshold=3%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
- --- modulename: output-bmk-results, funcname: get_threshold
-output-bmk-results.py(98):     if metric == "sample":
-output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
- --- modulename: output-bmk-results, funcname: get_specific_thresholds
-output-bmk-results.py(57):     if specific_variability is None:
-output-bmk-results.py(60):     var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)]
-output-bmk-results.py(61):     if var.empty:
-output-bmk-results.py(63):     elif len(var)>1:
-output-bmk-results.py(68):     if var.iloc[0]['sample_variation_average']>0 :
-output-bmk-results.py(69):        threshold = ( var.iloc[0]['sample_variation_average'] )
-output-bmk-results.py(70):        if mode == "build":
-output-bmk-results.py(74):           threshold *= 3
-output-bmk-results.py(81):        return threshold
-output-bmk-results.py(100):        if not np.isnan(spec_thr):
-output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
-output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking exe.regression : 483.xalancbmk,Xalan_base.default : sample=0% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
- --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(245):     f_out.close()
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(253):     f_out.close()
  --- modulename: output-bmk-results, funcname: close
 output-bmk-results.py(29):     if not self.outf:
 output-bmk-results.py(31):     self.outf.close()
 output-bmk-results.py(32):     if os.stat(self.filename).st_size == 0:
 output-bmk-results.py(33):        os.remove(self.filename)
-output-bmk-results.py(263):     output_bmk_results_1(exe_df, "exe", "improvement", None, f_skip, None, run_step_artifacts, metric, mode, details)
+output-bmk-results.py(298):     output_bmk_results_1(exe_df, "exe", "improvement", None, f_skip, None, run_step_artifacts, metric, mode, details)
  --- modulename: output-bmk-results, funcname: output_bmk_results_1
-output-bmk-results.py(210):     f_out = Outfile("{0}/{1}.{2}".format(run_step_artifacts, sym_type, change_kind), "w", predicate=(details=="verbose"))
+output-bmk-results.py(218):     f_out = Outfile("{0}/{1}.{2}".format(run_step_artifacts, sym_type, change_kind), "w", predicate=(details=="verbose"))
  --- modulename: output-bmk-results, funcname: __init__
 output-bmk-results.py(19):     self.filename=filename
 output-bmk-results.py(20):     self.predicate=predicate
 output-bmk-results.py(21):     if predicate:
 output-bmk-results.py(22):        self.outf = open(filename, mode)
 output-bmk-results.py(23):        self.csvwriter = csv.writer(self.outf)
-output-bmk-results.py(212):     rel_metric = "rel_" + metric
-output-bmk-results.py(213):     out_df = results_df[results_df[rel_metric] != -1]
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(220):     rel_metric = "rel_" + metric
+output-bmk-results.py(221):     out_df = results_df[results_df[rel_metric] != -1]
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -918,17 +1805,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking exe.improvement : 400.perlbench,perlbench_base.default : sample=0% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -945,17 +1832,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking exe.improvement : 401.bzip2,bzip2_base.default : sample=2% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking exe.improvement : 401.bzip2,bzip2_base.default : sample=1% (threshold=3%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -972,17 +1859,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking exe.improvement : 403.gcc,gcc_base.default : sample=0% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -999,17 +1886,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking exe.improvement : 410.bwaves,bwaves_base.default : sample=0% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -1026,17 +1913,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking exe.improvement : 416.gamess,gamess_base.default : sample=1% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking exe.improvement : 416.gamess,gamess_base.default : sample=0% (threshold=3%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -1053,17 +1940,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking exe.improvement : 429.mcf,mcf_base.default : sample=0% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -1080,17 +1967,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking exe.improvement : 433.milc,milc_base.default : sample=0% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -1107,17 +1994,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking exe.improvement : 434.zeusmp,zeusmp_base.default : sample=0% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -1134,17 +2021,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking exe.improvement : 435.gromacs,gromacs_base.default : sample=0% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -1161,17 +2048,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking exe.improvement : 436.cactusADM,cactusADM_base.default : sample=0% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -1188,17 +2075,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking exe.improvement : 437.leslie3d,leslie3d_base.default : sample=1% (threshold=10.44%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking exe.improvement : 437.leslie3d,leslie3d_base.default : sample=0% (threshold=10.5%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -1215,17 +2102,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking exe.improvement : 444.namd,namd_base.default : sample=0% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -1242,17 +2129,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking exe.improvement : 445.gobmk,gobmk_base.default : sample=0% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -1269,17 +2156,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking exe.improvement : 447.dealII,dealII_base.default : sample=0% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -1296,17 +2183,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking exe.improvement : 450.soplex,soplex_base.default : sample=0% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -1323,17 +2210,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking exe.improvement : 453.povray,povray_base.default : sample=0% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -1350,17 +2237,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking exe.improvement : 454.calculix,calculix_base.default : sample=0% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -1377,17 +2264,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking exe.improvement : 456.hmmer,hmmer_base.default : sample=-2% (threshold=3.96%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking exe.improvement : 456.hmmer,hmmer_base.default : sample=2% (threshold=3.99%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -1404,17 +2291,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking exe.improvement : 458.sjeng,sjeng_base.default : sample=0% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -1431,17 +2318,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking exe.improvement : 459.GemsFDTD,GemsFDTD_base.default : sample=-1% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking exe.improvement : 459.GemsFDTD,GemsFDTD_base.default : sample=0% (threshold=3%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -1458,17 +2345,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking exe.improvement : 462.libquantum,libquantum_base.default : sample=1% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking exe.improvement : 462.libquantum,libquantum_base.default : sample=-1% (threshold=3%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -1485,17 +2372,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking exe.improvement : 464.h264ref,h264ref_base.default : sample=-1% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking exe.improvement : 464.h264ref,h264ref_base.default : sample=1% (threshold=3%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -1512,17 +2399,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking exe.improvement : 465.tonto,tonto_base.default : sample=0% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -1539,17 +2426,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking exe.improvement : 470.lbm,lbm_base.default : sample=0% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking exe.improvement : 470.lbm,lbm_base.default : sample=-1% (threshold=3%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -1566,17 +2453,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking exe.improvement : 471.omnetpp,omnetpp_base.default : sample=0% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -1593,17 +2480,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking exe.improvement : 473.astar,astar_base.default : sample=0% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -1620,44 +2507,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking exe.improvement : 481.wrf,wrf_base.default : sample=1% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
- --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
- --- modulename: output-bmk-results, funcname: get_threshold
-output-bmk-results.py(98):     if metric == "sample":
-output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
- --- modulename: output-bmk-results, funcname: get_specific_thresholds
-output-bmk-results.py(57):     if specific_variability is None:
-output-bmk-results.py(60):     var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)]
-output-bmk-results.py(61):     if var.empty:
-output-bmk-results.py(63):     elif len(var)>1:
-output-bmk-results.py(68):     if var.iloc[0]['sample_variation_average']>0 :
-output-bmk-results.py(69):        threshold = ( var.iloc[0]['sample_variation_average'] )
-output-bmk-results.py(70):        if mode == "build":
-output-bmk-results.py(74):           threshold *= 3
-output-bmk-results.py(81):        return threshold
-output-bmk-results.py(100):        if not np.isnan(spec_thr):
-output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
-output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking exe.improvement : 482.sphinx3,sphinx_livepretend_base.default : sample=0% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -1674,35 +2534,35 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking exe.improvement : 483.xalancbmk,Xalan_base.default : sample=0% (threshold=3%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking exe.improvement : 482.sphinx3,sphinx_livepretend_base.default : sample=-2% (threshold=3%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(245):     f_out.close()
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(253):     f_out.close()
  --- modulename: output-bmk-results, funcname: close
 output-bmk-results.py(29):     if not self.outf:
 output-bmk-results.py(31):     self.outf.close()
 output-bmk-results.py(32):     if os.stat(self.filename).st_size == 0:
 output-bmk-results.py(33):        os.remove(self.filename)
-output-bmk-results.py(265):     output_bmk_results_1(sym_df, "symbol", "regression", f_regr, f_skip, f_ebp, run_step_artifacts, metric, mode, details)
+output-bmk-results.py(300):     output_bmk_results_1(sym_df, "symbol", "regression", f_regr, f_skip, f_ebp, run_step_artifacts, metric, mode, details)
  --- modulename: output-bmk-results, funcname: output_bmk_results_1
-output-bmk-results.py(210):     f_out = Outfile("{0}/{1}.{2}".format(run_step_artifacts, sym_type, change_kind), "w", predicate=(details=="verbose"))
+output-bmk-results.py(218):     f_out = Outfile("{0}/{1}.{2}".format(run_step_artifacts, sym_type, change_kind), "w", predicate=(details=="verbose"))
  --- modulename: output-bmk-results, funcname: __init__
 output-bmk-results.py(19):     self.filename=filename
 output-bmk-results.py(20):     self.predicate=predicate
 output-bmk-results.py(21):     if predicate:
 output-bmk-results.py(22):        self.outf = open(filename, mode)
 output-bmk-results.py(23):        self.csvwriter = csv.writer(self.outf)
-output-bmk-results.py(212):     rel_metric = "rel_" + metric
-output-bmk-results.py(213):     out_df = results_df[results_df[rel_metric] != -1]
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(220):     rel_metric = "rel_" + metric
+output-bmk-results.py(221):     out_df = results_df[results_df[rel_metric] != -1]
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -1719,17 +2579,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 400.perlbench,[.] S_regmatch : sample=1% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 400.perlbench,[.] S_regmatch : sample=0% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -1741,17 +2601,17 @@ output-bmk-results.py(62):         return np.nan
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(107):     if metric == "num_vect_loops" or metric == "num_sve_loops":
 output-bmk-results.py(110):     return default_threshold[(change_kind,metric,mode)]
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 400.perlbench,libc.so.6 : sample=5% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 400.perlbench,libc.so.6 : sample=-5% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -1768,17 +2628,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 401.bzip2,[.] mainSort : sample=8% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 401.bzip2,[.] mainSort : sample=-3% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -1795,17 +2655,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 401.bzip2,[.] mainGtU.part.0 : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 401.bzip2,[.] mainGtU.part.0 : sample=3% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -1822,17 +2682,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking symbol.regression : 401.bzip2,[.] fallbackSort : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -1849,17 +2709,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 401.bzip2,[.] BZ2_compressBlock : sample=-4% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 401.bzip2,[.] BZ2_compressBlock : sample=8% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -1876,17 +2736,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 401.bzip2,[.] BZ2_decompress : sample=4% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 401.bzip2,[.] BZ2_decompress : sample=-2% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -1903,17 +2763,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 403.gcc,[.] reg_is_remote_constant_p : sample=-2% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 403.gcc,libc.so.6 : sample=1% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -1930,17 +2790,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 403.gcc,libc.so.6 : sample=-2% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 403.gcc,[.] reg_is_remote_constant_p : sample=2% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -1957,17 +2817,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 403.gcc,[.] memset : sample=-4% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 403.gcc,[.] memset : sample=3% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -1984,17 +2844,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 410.bwaves,[.] mat_times_vec_ : sample=-1% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 410.bwaves,[.] mat_times_vec_ : sample=0% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -2011,17 +2871,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 410.bwaves,[.] bi_cgstab_block_ : sample=3% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 410.bwaves,[.] bi_cgstab_block_ : sample=1% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -2038,17 +2898,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 416.gamess,[.] forms_ : sample=2% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 416.gamess,[.] forms_ : sample=-2% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -2065,17 +2925,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 416.gamess,[.] twotff_ : sample=1% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 416.gamess,[.] twotff_ : sample=0% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -2092,17 +2952,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 416.gamess,[.] dirfck_ : sample=-1% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 416.gamess,[.] dirfck_ : sample=3% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -2119,17 +2979,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 416.gamess,[.] xyzint_ : sample=3% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 416.gamess,[.] xyzint_ : sample=-2% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -2146,17 +3006,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 429.mcf,[.] primal_bea_mpp : sample=1% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 429.mcf,[.] primal_bea_mpp : sample=0% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -2173,17 +3033,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 429.mcf,[.] refresh_potential : sample=-3% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 429.mcf,[.] refresh_potential : sample=0% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -2200,17 +3060,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 433.milc,[.] mult_su3_na : sample=2% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 433.milc,[.] mult_su3_na : sample=-1% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -2227,17 +3087,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 433.milc,[.] mult_su3_nn : sample=-2% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 433.milc,[.] mult_su3_nn : sample=0% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -2254,17 +3114,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 433.milc,[.] mult_su3_mat_vec : sample=1% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 433.milc,[.] mult_su3_mat_vec : sample=-1% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -2281,17 +3141,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 433.milc,[.] mult_adj_su3_mat_vec : sample=3% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 433.milc,[.] mult_adj_su3_mat_vec : sample=-2% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -2308,17 +3168,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 433.milc,[.] uncompress_anti_hermitian : sample=-4% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 433.milc,[.] uncompress_anti_hermitian : sample=-3% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -2335,17 +3195,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 434.zeusmp,[.] hsmoc_ : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 434.zeusmp,[.] hsmoc_ : sample=-1% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -2362,17 +3222,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking symbol.regression : 434.zeusmp,[.] lorentz_ : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -2389,17 +3249,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 435.gromacs,[.] inl1130_ : sample=1% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 435.gromacs,[.] inl1130_ : sample=-1% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -2416,17 +3276,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking symbol.regression : 435.gromacs,[.] search_neighbours : sample=1% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -2443,17 +3303,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking symbol.regression : 436.cactusADM,[.] bench_staggeredleapfrog2_ : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -2470,17 +3330,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking symbol.regression : 437.leslie3d,[.] fluxk_ : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -2497,17 +3357,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking symbol.regression : 437.leslie3d,[.] fluxj_ : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -2524,17 +3384,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 437.leslie3d,[.] fluxi_ : sample=-1% (threshold=15.059999999999999%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 437.leslie3d,[.] fluxi_ : sample=0% (threshold=15.09%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -2551,17 +3411,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 437.leslie3d,[.] extrapi_ : sample=1% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 437.leslie3d,[.] extrapi_ : sample=-1% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -2578,17 +3438,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 437.leslie3d,[.] extrapj_ : sample=2% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 437.leslie3d,[.] extrapj_ : sample=-1% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -2605,17 +3465,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking symbol.regression : 444.namd,[.] _ZN20ComputeNonbondedUtil26calc_pair_energy_fullelectEP9nonbonded : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -2632,17 +3492,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking symbol.regression : 444.namd,[.] _ZN20ComputeNonbondedUtil19calc_pair_fullelectEP9nonbonded : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -2659,17 +3519,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking symbol.regression : 444.namd,[.] _ZN20ComputeNonbondedUtil32calc_pair_energy_merge_fullelectEP9nonbonded : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -2686,17 +3546,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking symbol.regression : 444.namd,[.] _ZN20ComputeNonbondedUtil16calc_pair_energyEP9nonbonded : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -2713,17 +3573,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking symbol.regression : 444.namd,[.] _ZN20ComputeNonbondedUtil9calc_pairEP9nonbonded : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -2740,17 +3600,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking symbol.regression : 444.namd,[.] _ZN20ComputeNonbondedUtil25calc_pair_merge_fullelectEP9nonbonded : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -2767,17 +3627,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking symbol.regression : 445.gobmk,[.] do_play_move : sample=-2% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -2794,17 +3654,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 445.gobmk,[.] fastlib : sample=1% (threshold=15.120000000000001%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 445.gobmk,[.] fastlib : sample=-11% (threshold=15.120000000000001%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -2821,17 +3681,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 447.dealII,[.] _ZNK9MappingQ1ILi3EE12compute_fillERK12TriaIteratorILi3E15DoFCellAccessorILi3EEEjN10QProjectorILi3EE17DataSetDescriptorERNS0_12InternalDataERSt6vectorI5PointILi3EESaISE_EE : sample=-1% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 447.dealII,[.] _ZNK9MappingQ1ILi3EE12compute_fillERK12TriaIteratorILi3E15DoFCellAccessorILi3EEEjN10QProjectorILi3EE17DataSetDescriptorERNS0_12InternalDataERSt6vectorI5PointILi3EESaISE_EE : sample=1% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -2848,17 +3708,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 447.dealII,[.] _ZNK12SparseMatrixIdE5vmultI6VectorIdES3_EEvRT_RKT0_ : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 447.dealII,[.] _ZNK12SparseMatrixIdE5vmultI6VectorIdES3_EEvRT_RKT0_ : sample=2% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -2875,17 +3735,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 447.dealII,libstdc++.so.6.0.33 : sample=1% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 447.dealII,libstdc++.so.6.0.33 : sample=-2% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -2902,17 +3762,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 447.dealII,[.] _ZN13LaplaceSolver6SolverILi3EE22assemble_linear_systemERNS1_12LinearSystemE : sample=4% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 447.dealII,[.] _ZN13LaplaceSolver6SolverILi3EE22assemble_linear_systemERNS1_12LinearSystemE : sample=-3% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -2929,17 +3789,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 447.dealII,[.] _ZSt18_Rb_tree_incrementPKSt18_Rb_tree_node_base : sample=5% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 447.dealII,[.] _ZSt18_Rb_tree_incrementPKSt18_Rb_tree_node_base : sample=-3% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -2956,17 +3816,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 450.soplex,[.] _ZN6soplex10SPxSteepPR8entered4ENS_5SPxIdEi : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 450.soplex,[.] _ZN6soplex10SPxSteepPR8entered4ENS_5SPxIdEi : sample=2% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -2983,17 +3843,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 450.soplex,[.] _ZN6soplex8SSVector18assign2productFullERKNS_5SVSetERKS0_ : sample=-1% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 450.soplex,[.] _ZN6soplex8SSVector18assign2productFullERKNS_5SVSetERKS0_ : sample=-2% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -3010,17 +3870,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 450.soplex,[.] _ZN6soplex9CLUFactor16initFactorMatrixEPPNS_7SVectorEd : sample=-1% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 450.soplex,[.] _ZN6soplex9CLUFactor16initFactorMatrixEPPNS_7SVectorEd : sample=7% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -3037,17 +3897,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 453.povray,[.] _ZN3povL23All_Plane_IntersectionsEPNS_13Object_StructEPNS_10Ray_StructEPNS_13istack_structE : sample=1% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 453.povray,[.] _ZN3povL23All_Plane_IntersectionsEPNS_13Object_StructEPNS_10Ray_StructEPNS_13istack_structE : sample=3% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -3064,17 +3924,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 453.povray,[.] _ZN3povL31All_CSG_Intersect_IntersectionsEPNS_13Object_StructEPNS_10Ray_StructEPNS_13istack_structE : sample=-1% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 453.povray,[.] _ZN3povL31All_CSG_Intersect_IntersectionsEPNS_13Object_StructEPNS_10Ray_StructEPNS_13istack_structE : sample=5% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -3091,17 +3951,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 453.povray,[.] _ZN3povL24All_Sphere_IntersectionsEPNS_13Object_StructEPNS_10Ray_StructEPNS_13istack_structE : sample=7% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 453.povray,[.] _ZN3povL24All_Sphere_IntersectionsEPNS_13Object_StructEPNS_10Ray_StructEPNS_13istack_structE : sample=-9% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -3118,17 +3978,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 453.povray,[.] _ZN3pov17Check_And_EnqueueEPNS_21Priority_Queue_StructEPNS_16BBox_Tree_StructEPNS_19Bounding_Box_StructEPNS_14Rayinfo_StructE : sample=-5% (threshold=15.330000000000002%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 453.povray,[.] _ZN3pov17Check_And_EnqueueEPNS_21Priority_Queue_StructEPNS_16BBox_Tree_StructEPNS_19Bounding_Box_StructEPNS_14Rayinfo_StructE : sample=1% (threshold=15.330000000000002%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -3145,17 +4005,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 454.calculix,[.] e_c3d_ : sample=-1% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 454.calculix,[.] e_c3d_ : sample=0% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -3172,17 +4032,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking symbol.regression : 454.calculix,[.] DVdot33 : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -3199,17 +4059,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 456.hmmer,[.] P7Viterbi : sample=-3% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 456.hmmer,[.] P7Viterbi : sample=3% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -3226,17 +4086,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 458.sjeng,[.] std_eval : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 458.sjeng,[.] std_eval : sample=3% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -3253,47 +4113,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 458.sjeng,[.] gen : sample=-16% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 458.sjeng,[.] gen : sample=5% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(227):         percent_change, short_diag, long_diag = get_short_long_diag(row, metric, sym_type, change_kind)
- --- modulename: output-bmk-results, funcname: get_short_long_diag
-output-bmk-results.py(113):     bmk = row["benchmark"]
-output-bmk-results.py(114):     rel_value = row["rel_" + metric]
-output-bmk-results.py(115):     prev_value = row[metric + "_x"]
-output-bmk-results.py(116):     curr_value = row[metric + "_y"]
-output-bmk-results.py(118):     if metric == "sample":
-output-bmk-results.py(119):         if curr_value == 999999999:
-output-bmk-results.py(122):         elif curr_value == 888888888:
-output-bmk-results.py(125):         elif prev_value == 999999999 and curr_value == 888888888:
-output-bmk-results.py(128):         elif prev_value == 888888888 and curr_value < 888888888:
-output-bmk-results.py(131):         elif prev_value == 999999999 and curr_value < 888888888:
-output-bmk-results.py(144):     suffix = ""
-output-bmk-results.py(145):     if metric == "sample":
-output-bmk-results.py(146):         prefix_regression = "slowed down by"
-output-bmk-results.py(147):         prefix_improvement = "sped up by"
-output-bmk-results.py(148):         suffix = "perf samples"
-output-bmk-results.py(159):     if sym_type=="symbol":
-output-bmk-results.py(160):       item=bmk+":"+row["symbol"]
-output-bmk-results.py(164):     short_diag = "{1} {2}% - {0}".format(item, locals()["prefix_" + change_kind], abs(rel_value - 100))
-output-bmk-results.py(165):     long_diag = "{0} - from {1} to {2} {3}".format(short_diag, prev_value, curr_value, suffix)
-output-bmk-results.py(166):     return abs(rel_value - 100), short_diag, long_diag
-output-bmk-results.py(231):         if metric == "sample" \
-output-bmk-results.py(232):           and row['symbol_md5sum_x'] == row['symbol_md5sum_y'] \
-output-bmk-results.py(233):           and row['symbol_md5sum_x'] != "-1" \
-output-bmk-results.py(234):           and row['symbol_md5sum_x'] != "d41d8cd98f00b204e9800998ecf8427e":
-output-bmk-results.py(235):             f_skip.write_csv((row["benchmark"], row["symbol"], short_diag, long_diag))
- --- modulename: output-bmk-results, funcname: write_csv
-output-bmk-results.py(41):     if not self.predicate or not self.csvwriter:
-output-bmk-results.py(43):     self.csvwriter.writerow(arr)
-output-bmk-results.py(236):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -3310,17 +4140,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 458.sjeng,[.] setup_attackers : sample=2% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 458.sjeng,[.] setup_attackers : sample=1% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -3337,17 +4167,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 459.GemsFDTD,[.] __upml_mod_MOD_upmlupdatee : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 459.GemsFDTD,[.] __upml_mod_MOD_upmlupdatee : sample=-1% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -3364,17 +4194,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 459.GemsFDTD,[.] __nft_mod_MOD_nft_store : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 459.GemsFDTD,[.] __nft_mod_MOD_nft_store : sample=1% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -3391,17 +4221,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 459.GemsFDTD,[.] __upml_mod_MOD_upmlupdateh : sample=-2% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 459.GemsFDTD,[.] __upml_mod_MOD_upmlupdateh : sample=0% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -3418,17 +4248,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 459.GemsFDTD,[.] __update_mod_MOD_updatee_homo : sample=-3% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 459.GemsFDTD,[.] __update_mod_MOD_updatee_homo : sample=1% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -3445,17 +4275,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking symbol.regression : 459.GemsFDTD,[.] __update_mod_MOD_updateh_homo : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -3472,17 +4302,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 462.libquantum,[.] quantum_toffoli : sample=1% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 462.libquantum,[.] quantum_toffoli : sample=0% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -3499,17 +4329,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 462.libquantum,[.] quantum_sigma_x : sample=1% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 462.libquantum,[.] quantum_sigma_x : sample=-4% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -3526,17 +4356,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 462.libquantum,[.] quantum_cnot : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 462.libquantum,[.] quantum_cnot : sample=3% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -3553,17 +4383,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 464.h264ref,[.] SetupFastFullPelSearch : sample=-1% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 464.h264ref,[.] SetupFastFullPelSearch : sample=1% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -3580,17 +4410,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 464.h264ref,libc.so.6 : sample=5% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 464.h264ref,libc.so.6 : sample=-3% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -3607,17 +4437,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 464.h264ref,[.] __memcpy_neon : sample=5% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 464.h264ref,[.] __memcpy_neon : sample=-3% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -3634,17 +4464,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 464.h264ref,[.] FastFullPelBlockMotionSearch : sample=-1% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 464.h264ref,[.] FastFullPelBlockMotionSearch : sample=5% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -3656,17 +4486,17 @@ output-bmk-results.py(62):         return np.nan
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(107):     if metric == "num_vect_loops" or metric == "num_sve_loops":
 output-bmk-results.py(110):     return default_threshold[(change_kind,metric,mode)]
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 465.tonto,libm.so.6 : sample=2% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 465.tonto,libm.so.6 : sample=0% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -3683,17 +4513,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 465.tonto,[.] __shell2_module_MOD_make_ft_1 : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 465.tonto,[.] __shell2_module_MOD_make_ft_1 : sample=-9% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -3705,17 +4535,17 @@ output-bmk-results.py(62):         return np.nan
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(107):     if metric == "num_vect_loops" or metric == "num_sve_loops":
 output-bmk-results.py(110):     return default_threshold[(change_kind,metric,mode)]
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 465.tonto,[.] __sincosl : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 465.tonto,[.] __sincosl : sample=5% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -3732,17 +4562,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 465.tonto,[.] __shell1quartet_module_MOD_make_esfs.isra.0 : sample=-4% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 465.tonto,[.] __shell1quartet_module_MOD_make_esfs.isra.0 : sample=1% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -3754,17 +4584,17 @@ output-bmk-results.py(62):         return np.nan
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(107):     if metric == "num_vect_loops" or metric == "num_sve_loops":
 output-bmk-results.py(110):     return default_threshold[(change_kind,metric,mode)]
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 465.tonto,[.] __cexpl : sample=1% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 465.tonto,[.] __cexpl : sample=-4% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -3781,17 +4611,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 470.lbm,[.] LBM_performStreamCollide : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 470.lbm,[.] LBM_performStreamCollide : sample=-1% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -3808,17 +4638,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking symbol.regression : 471.omnetpp,[.] _ZN12cMessageHeap8getFirstEv : sample=-3% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -3830,17 +4660,17 @@ output-bmk-results.py(62):         return np.nan
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(107):     if metric == "num_vect_loops" or metric == "num_sve_loops":
 output-bmk-results.py(110):     return default_threshold[(change_kind,metric,mode)]
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 471.omnetpp,libc.so.6 : sample=2% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 471.omnetpp,libc.so.6 : sample=-3% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -3857,17 +4687,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 471.omnetpp,[.] _ZN5cGate7deliverEP8cMessaged : sample=-3% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 471.omnetpp,[.] _ZN5cGate7deliverEP8cMessaged : sample=1% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -3884,17 +4714,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking symbol.regression : 473.astar,[.] _ZN6wayobj10makebound2EPiiS0_ : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -3911,17 +4741,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 473.astar,[.] _ZN7way2obj12releasepointEii : sample=2% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 473.astar,[.] _ZN7way2obj12releasepointEii : sample=0% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -3938,17 +4768,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 473.astar,[.] _ZN9regwayobj10makebound2ER9flexarrayIP6regobjES4_ : sample=-2% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 473.astar,[.] _ZN9regwayobj10makebound2ER9flexarrayIP6regobjES4_ : sample=2% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -3965,17 +4795,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 481.wrf,[.] __module_advect_em_MOD_advect_scalar : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 481.wrf,[.] __module_advect_em_MOD_advect_scalar : sample=-6% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -3992,17 +4822,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 481.wrf,[.] __module_small_step_em_MOD_advance_w : sample=10% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 481.wrf,[.] __module_small_step_em_MOD_advance_w : sample=-4% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -4019,17 +4849,39 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 481.wrf,libm.so.6 : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 481.wrf,libm.so.6 : sample=-1% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+ --- modulename: output-bmk-results, funcname: get_threshold
+output-bmk-results.py(98):     if metric == "sample":
+output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
+ --- modulename: output-bmk-results, funcname: get_specific_thresholds
+output-bmk-results.py(57):     if specific_variability is None:
+output-bmk-results.py(60):     var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)]
+output-bmk-results.py(61):     if var.empty:
+output-bmk-results.py(62):         return np.nan
+output-bmk-results.py(100):        if not np.isnan(spec_thr):
+output-bmk-results.py(107):     if metric == "num_vect_loops" or metric == "num_sve_loops":
+output-bmk-results.py(110):     return default_threshold[(change_kind,metric,mode)]
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 481.wrf,libc.so.6 : sample=-10% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+ --- modulename: output-bmk-results, funcname: is_entry_regression
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -4046,17 +4898,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 481.wrf,[.] powf@@GLIBC_2.27 : sample=1% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 481.wrf,[.] __module_small_step_em_MOD_advance_uv : sample=-12% (threshold=21.18%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -4073,17 +4925,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking symbol.regression : 482.sphinx3,[.] vector_gautbl_eval_logs3 : sample=-2% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -4100,35 +4952,35 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.regression : 482.sphinx3,[.] mgau_eval : sample=2% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.regression : 482.sphinx3,[.] mgau_eval : sample=-2% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_regression
-output-bmk-results.py(175):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(176):         return (result - 100 > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(245):     f_out.close()
+output-bmk-results.py(183):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(184):         return (result - 100 > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(253):     f_out.close()
  --- modulename: output-bmk-results, funcname: close
 output-bmk-results.py(29):     if not self.outf:
 output-bmk-results.py(31):     self.outf.close()
 output-bmk-results.py(32):     if os.stat(self.filename).st_size == 0:
 output-bmk-results.py(33):        os.remove(self.filename)
-output-bmk-results.py(266):     output_bmk_results_1(sym_df, "symbol", "improvement", None, f_skip, None, run_step_artifacts, metric, mode, details)
+output-bmk-results.py(301):     output_bmk_results_1(sym_df, "symbol", "improvement", None, f_skip, None, run_step_artifacts, metric, mode, details)
  --- modulename: output-bmk-results, funcname: output_bmk_results_1
-output-bmk-results.py(210):     f_out = Outfile("{0}/{1}.{2}".format(run_step_artifacts, sym_type, change_kind), "w", predicate=(details=="verbose"))
+output-bmk-results.py(218):     f_out = Outfile("{0}/{1}.{2}".format(run_step_artifacts, sym_type, change_kind), "w", predicate=(details=="verbose"))
  --- modulename: output-bmk-results, funcname: __init__
 output-bmk-results.py(19):     self.filename=filename
 output-bmk-results.py(20):     self.predicate=predicate
 output-bmk-results.py(21):     if predicate:
 output-bmk-results.py(22):        self.outf = open(filename, mode)
 output-bmk-results.py(23):        self.csvwriter = csv.writer(self.outf)
-output-bmk-results.py(212):     rel_metric = "rel_" + metric
-output-bmk-results.py(213):     out_df = results_df[results_df[rel_metric] != -1]
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(220):     rel_metric = "rel_" + metric
+output-bmk-results.py(221):     out_df = results_df[results_df[rel_metric] != -1]
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -4145,17 +4997,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 400.perlbench,[.] S_regmatch : sample=1% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 400.perlbench,[.] S_regmatch : sample=0% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -4167,17 +5019,17 @@ output-bmk-results.py(62):         return np.nan
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(107):     if metric == "num_vect_loops" or metric == "num_sve_loops":
 output-bmk-results.py(110):     return default_threshold[(change_kind,metric,mode)]
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 400.perlbench,libc.so.6 : sample=5% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 400.perlbench,libc.so.6 : sample=-5% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -4194,17 +5046,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 401.bzip2,[.] mainSort : sample=8% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 401.bzip2,[.] mainSort : sample=-3% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -4221,17 +5073,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 401.bzip2,[.] mainGtU.part.0 : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 401.bzip2,[.] mainGtU.part.0 : sample=3% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -4248,17 +5100,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking symbol.improvement : 401.bzip2,[.] fallbackSort : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -4275,17 +5127,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 401.bzip2,[.] BZ2_compressBlock : sample=-4% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 401.bzip2,[.] BZ2_compressBlock : sample=8% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -4302,17 +5154,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 401.bzip2,[.] BZ2_decompress : sample=4% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 401.bzip2,[.] BZ2_decompress : sample=-2% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -4329,17 +5181,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 403.gcc,[.] reg_is_remote_constant_p : sample=-2% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 403.gcc,libc.so.6 : sample=1% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -4356,17 +5208,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 403.gcc,libc.so.6 : sample=-2% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 403.gcc,[.] reg_is_remote_constant_p : sample=2% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -4383,17 +5235,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 403.gcc,[.] memset : sample=-4% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 403.gcc,[.] memset : sample=3% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -4410,17 +5262,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 410.bwaves,[.] mat_times_vec_ : sample=-1% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 410.bwaves,[.] mat_times_vec_ : sample=0% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -4437,17 +5289,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 410.bwaves,[.] bi_cgstab_block_ : sample=3% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 410.bwaves,[.] bi_cgstab_block_ : sample=1% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -4464,17 +5316,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 416.gamess,[.] forms_ : sample=2% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 416.gamess,[.] forms_ : sample=-2% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -4491,17 +5343,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 416.gamess,[.] twotff_ : sample=1% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 416.gamess,[.] twotff_ : sample=0% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -4518,17 +5370,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 416.gamess,[.] dirfck_ : sample=-1% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 416.gamess,[.] dirfck_ : sample=3% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -4545,17 +5397,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 416.gamess,[.] xyzint_ : sample=3% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 416.gamess,[.] xyzint_ : sample=-2% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -4572,17 +5424,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 429.mcf,[.] primal_bea_mpp : sample=1% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 429.mcf,[.] primal_bea_mpp : sample=0% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -4599,17 +5451,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 429.mcf,[.] refresh_potential : sample=-3% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 429.mcf,[.] refresh_potential : sample=0% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -4626,17 +5478,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 433.milc,[.] mult_su3_na : sample=2% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 433.milc,[.] mult_su3_na : sample=-1% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -4653,17 +5505,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 433.milc,[.] mult_su3_nn : sample=-2% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 433.milc,[.] mult_su3_nn : sample=0% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -4680,17 +5532,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 433.milc,[.] mult_su3_mat_vec : sample=1% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 433.milc,[.] mult_su3_mat_vec : sample=-1% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -4707,17 +5559,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 433.milc,[.] mult_adj_su3_mat_vec : sample=3% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 433.milc,[.] mult_adj_su3_mat_vec : sample=-2% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -4734,17 +5586,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 433.milc,[.] uncompress_anti_hermitian : sample=-4% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 433.milc,[.] uncompress_anti_hermitian : sample=-3% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -4761,17 +5613,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 434.zeusmp,[.] hsmoc_ : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 434.zeusmp,[.] hsmoc_ : sample=-1% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -4788,17 +5640,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking symbol.improvement : 434.zeusmp,[.] lorentz_ : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -4815,17 +5667,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 435.gromacs,[.] inl1130_ : sample=1% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 435.gromacs,[.] inl1130_ : sample=-1% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -4842,17 +5694,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking symbol.improvement : 435.gromacs,[.] search_neighbours : sample=1% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -4869,17 +5721,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking symbol.improvement : 436.cactusADM,[.] bench_staggeredleapfrog2_ : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -4896,17 +5748,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking symbol.improvement : 437.leslie3d,[.] fluxk_ : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -4923,17 +5775,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking symbol.improvement : 437.leslie3d,[.] fluxj_ : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -4950,17 +5802,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 437.leslie3d,[.] fluxi_ : sample=-1% (threshold=15.059999999999999%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 437.leslie3d,[.] fluxi_ : sample=0% (threshold=15.09%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -4977,17 +5829,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 437.leslie3d,[.] extrapi_ : sample=1% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 437.leslie3d,[.] extrapi_ : sample=-1% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -5004,17 +5856,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 437.leslie3d,[.] extrapj_ : sample=2% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 437.leslie3d,[.] extrapj_ : sample=-1% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -5031,17 +5883,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking symbol.improvement : 444.namd,[.] _ZN20ComputeNonbondedUtil26calc_pair_energy_fullelectEP9nonbonded : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -5058,17 +5910,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking symbol.improvement : 444.namd,[.] _ZN20ComputeNonbondedUtil19calc_pair_fullelectEP9nonbonded : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -5085,17 +5937,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking symbol.improvement : 444.namd,[.] _ZN20ComputeNonbondedUtil32calc_pair_energy_merge_fullelectEP9nonbonded : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -5112,17 +5964,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking symbol.improvement : 444.namd,[.] _ZN20ComputeNonbondedUtil16calc_pair_energyEP9nonbonded : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -5139,17 +5991,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking symbol.improvement : 444.namd,[.] _ZN20ComputeNonbondedUtil9calc_pairEP9nonbonded : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -5166,17 +6018,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking symbol.improvement : 444.namd,[.] _ZN20ComputeNonbondedUtil25calc_pair_merge_fullelectEP9nonbonded : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -5193,17 +6045,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking symbol.improvement : 445.gobmk,[.] do_play_move : sample=-2% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -5220,17 +6072,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 445.gobmk,[.] fastlib : sample=1% (threshold=15.120000000000001%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 445.gobmk,[.] fastlib : sample=-11% (threshold=15.120000000000001%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -5247,17 +6099,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 447.dealII,[.] _ZNK9MappingQ1ILi3EE12compute_fillERK12TriaIteratorILi3E15DoFCellAccessorILi3EEEjN10QProjectorILi3EE17DataSetDescriptorERNS0_12InternalDataERSt6vectorI5PointILi3EESaISE_EE : sample=-1% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 447.dealII,[.] _ZNK9MappingQ1ILi3EE12compute_fillERK12TriaIteratorILi3E15DoFCellAccessorILi3EEEjN10QProjectorILi3EE17DataSetDescriptorERNS0_12InternalDataERSt6vectorI5PointILi3EESaISE_EE : sample=1% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -5274,17 +6126,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 447.dealII,[.] _ZNK12SparseMatrixIdE5vmultI6VectorIdES3_EEvRT_RKT0_ : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 447.dealII,[.] _ZNK12SparseMatrixIdE5vmultI6VectorIdES3_EEvRT_RKT0_ : sample=2% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -5301,17 +6153,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 447.dealII,libstdc++.so.6.0.33 : sample=1% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 447.dealII,libstdc++.so.6.0.33 : sample=-2% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -5328,17 +6180,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 447.dealII,[.] _ZN13LaplaceSolver6SolverILi3EE22assemble_linear_systemERNS1_12LinearSystemE : sample=4% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 447.dealII,[.] _ZN13LaplaceSolver6SolverILi3EE22assemble_linear_systemERNS1_12LinearSystemE : sample=-3% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -5355,17 +6207,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 447.dealII,[.] _ZSt18_Rb_tree_incrementPKSt18_Rb_tree_node_base : sample=5% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 447.dealII,[.] _ZSt18_Rb_tree_incrementPKSt18_Rb_tree_node_base : sample=-3% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -5382,17 +6234,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 450.soplex,[.] _ZN6soplex10SPxSteepPR8entered4ENS_5SPxIdEi : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 450.soplex,[.] _ZN6soplex10SPxSteepPR8entered4ENS_5SPxIdEi : sample=2% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -5409,17 +6261,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 450.soplex,[.] _ZN6soplex8SSVector18assign2productFullERKNS_5SVSetERKS0_ : sample=-1% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 450.soplex,[.] _ZN6soplex8SSVector18assign2productFullERKNS_5SVSetERKS0_ : sample=-2% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -5436,17 +6288,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 450.soplex,[.] _ZN6soplex9CLUFactor16initFactorMatrixEPPNS_7SVectorEd : sample=-1% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 450.soplex,[.] _ZN6soplex9CLUFactor16initFactorMatrixEPPNS_7SVectorEd : sample=7% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -5463,17 +6315,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 453.povray,[.] _ZN3povL23All_Plane_IntersectionsEPNS_13Object_StructEPNS_10Ray_StructEPNS_13istack_structE : sample=1% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 453.povray,[.] _ZN3povL23All_Plane_IntersectionsEPNS_13Object_StructEPNS_10Ray_StructEPNS_13istack_structE : sample=3% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -5490,17 +6342,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 453.povray,[.] _ZN3povL31All_CSG_Intersect_IntersectionsEPNS_13Object_StructEPNS_10Ray_StructEPNS_13istack_structE : sample=-1% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 453.povray,[.] _ZN3povL31All_CSG_Intersect_IntersectionsEPNS_13Object_StructEPNS_10Ray_StructEPNS_13istack_structE : sample=5% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -5517,17 +6369,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 453.povray,[.] _ZN3povL24All_Sphere_IntersectionsEPNS_13Object_StructEPNS_10Ray_StructEPNS_13istack_structE : sample=7% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 453.povray,[.] _ZN3povL24All_Sphere_IntersectionsEPNS_13Object_StructEPNS_10Ray_StructEPNS_13istack_structE : sample=-9% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -5544,17 +6396,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 453.povray,[.] _ZN3pov17Check_And_EnqueueEPNS_21Priority_Queue_StructEPNS_16BBox_Tree_StructEPNS_19Bounding_Box_StructEPNS_14Rayinfo_StructE : sample=-5% (threshold=15.330000000000002%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 453.povray,[.] _ZN3pov17Check_And_EnqueueEPNS_21Priority_Queue_StructEPNS_16BBox_Tree_StructEPNS_19Bounding_Box_StructEPNS_14Rayinfo_StructE : sample=1% (threshold=15.330000000000002%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -5571,17 +6423,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 454.calculix,[.] e_c3d_ : sample=-1% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 454.calculix,[.] e_c3d_ : sample=0% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -5598,17 +6450,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking symbol.improvement : 454.calculix,[.] DVdot33 : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -5625,17 +6477,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 456.hmmer,[.] P7Viterbi : sample=-3% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 456.hmmer,[.] P7Viterbi : sample=3% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -5652,17 +6504,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 458.sjeng,[.] std_eval : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 458.sjeng,[.] std_eval : sample=3% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -5679,17 +6531,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 458.sjeng,[.] gen : sample=-16% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 458.sjeng,[.] gen : sample=5% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -5706,17 +6558,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 458.sjeng,[.] setup_attackers : sample=2% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 458.sjeng,[.] setup_attackers : sample=1% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -5733,17 +6585,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 459.GemsFDTD,[.] __upml_mod_MOD_upmlupdatee : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 459.GemsFDTD,[.] __upml_mod_MOD_upmlupdatee : sample=-1% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -5760,17 +6612,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 459.GemsFDTD,[.] __nft_mod_MOD_nft_store : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 459.GemsFDTD,[.] __nft_mod_MOD_nft_store : sample=1% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -5787,17 +6639,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 459.GemsFDTD,[.] __upml_mod_MOD_upmlupdateh : sample=-2% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 459.GemsFDTD,[.] __upml_mod_MOD_upmlupdateh : sample=0% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -5814,17 +6666,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 459.GemsFDTD,[.] __update_mod_MOD_updatee_homo : sample=-3% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 459.GemsFDTD,[.] __update_mod_MOD_updatee_homo : sample=1% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -5841,17 +6693,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking symbol.improvement : 459.GemsFDTD,[.] __update_mod_MOD_updateh_homo : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -5868,17 +6720,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 462.libquantum,[.] quantum_toffoli : sample=1% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 462.libquantum,[.] quantum_toffoli : sample=0% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -5895,17 +6747,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 462.libquantum,[.] quantum_sigma_x : sample=1% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 462.libquantum,[.] quantum_sigma_x : sample=-4% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -5922,17 +6774,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 462.libquantum,[.] quantum_cnot : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 462.libquantum,[.] quantum_cnot : sample=3% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -5949,17 +6801,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 464.h264ref,[.] SetupFastFullPelSearch : sample=-1% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 464.h264ref,[.] SetupFastFullPelSearch : sample=1% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -5976,17 +6828,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 464.h264ref,libc.so.6 : sample=5% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 464.h264ref,libc.so.6 : sample=-3% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -6003,17 +6855,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 464.h264ref,[.] __memcpy_neon : sample=5% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 464.h264ref,[.] __memcpy_neon : sample=-3% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -6030,17 +6882,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 464.h264ref,[.] FastFullPelBlockMotionSearch : sample=-1% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 464.h264ref,[.] FastFullPelBlockMotionSearch : sample=5% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -6052,17 +6904,17 @@ output-bmk-results.py(62):         return np.nan
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(107):     if metric == "num_vect_loops" or metric == "num_sve_loops":
 output-bmk-results.py(110):     return default_threshold[(change_kind,metric,mode)]
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 465.tonto,libm.so.6 : sample=2% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 465.tonto,libm.so.6 : sample=0% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -6079,17 +6931,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 465.tonto,[.] __shell2_module_MOD_make_ft_1 : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 465.tonto,[.] __shell2_module_MOD_make_ft_1 : sample=-9% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -6101,17 +6953,17 @@ output-bmk-results.py(62):         return np.nan
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(107):     if metric == "num_vect_loops" or metric == "num_sve_loops":
 output-bmk-results.py(110):     return default_threshold[(change_kind,metric,mode)]
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 465.tonto,[.] __sincosl : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 465.tonto,[.] __sincosl : sample=5% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -6128,17 +6980,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 465.tonto,[.] __shell1quartet_module_MOD_make_esfs.isra.0 : sample=-4% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 465.tonto,[.] __shell1quartet_module_MOD_make_esfs.isra.0 : sample=1% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -6150,17 +7002,17 @@ output-bmk-results.py(62):         return np.nan
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(107):     if metric == "num_vect_loops" or metric == "num_sve_loops":
 output-bmk-results.py(110):     return default_threshold[(change_kind,metric,mode)]
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 465.tonto,[.] __cexpl : sample=1% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 465.tonto,[.] __cexpl : sample=-4% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -6177,17 +7029,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 470.lbm,[.] LBM_performStreamCollide : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 470.lbm,[.] LBM_performStreamCollide : sample=-1% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -6204,17 +7056,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking symbol.improvement : 471.omnetpp,[.] _ZN12cMessageHeap8getFirstEv : sample=-3% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -6226,17 +7078,17 @@ output-bmk-results.py(62):         return np.nan
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(107):     if metric == "num_vect_loops" or metric == "num_sve_loops":
 output-bmk-results.py(110):     return default_threshold[(change_kind,metric,mode)]
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 471.omnetpp,libc.so.6 : sample=2% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 471.omnetpp,libc.so.6 : sample=-3% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -6253,17 +7105,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 471.omnetpp,[.] _ZN5cGate7deliverEP8cMessaged : sample=-3% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 471.omnetpp,[.] _ZN5cGate7deliverEP8cMessaged : sample=1% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -6280,17 +7132,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking symbol.improvement : 473.astar,[.] _ZN6wayobj10makebound2EPiiS0_ : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -6307,17 +7159,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 473.astar,[.] _ZN7way2obj12releasepointEii : sample=2% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 473.astar,[.] _ZN7way2obj12releasepointEii : sample=0% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -6334,17 +7186,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 473.astar,[.] _ZN9regwayobj10makebound2ER9flexarrayIP6regobjES4_ : sample=-2% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 473.astar,[.] _ZN9regwayobj10makebound2ER9flexarrayIP6regobjES4_ : sample=2% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -6361,17 +7213,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 481.wrf,[.] __module_advect_em_MOD_advect_scalar : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 481.wrf,[.] __module_advect_em_MOD_advect_scalar : sample=-6% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -6388,17 +7240,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 481.wrf,[.] __module_small_step_em_MOD_advance_w : sample=10% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 481.wrf,[.] __module_small_step_em_MOD_advance_w : sample=-4% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -6415,17 +7267,39 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 481.wrf,libm.so.6 : sample=0% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 481.wrf,libm.so.6 : sample=-1% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+ --- modulename: output-bmk-results, funcname: get_threshold
+output-bmk-results.py(98):     if metric == "sample":
+output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
+ --- modulename: output-bmk-results, funcname: get_specific_thresholds
+output-bmk-results.py(57):     if specific_variability is None:
+output-bmk-results.py(60):     var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)]
+output-bmk-results.py(61):     if var.empty:
+output-bmk-results.py(62):         return np.nan
+output-bmk-results.py(100):        if not np.isnan(spec_thr):
+output-bmk-results.py(107):     if metric == "num_vect_loops" or metric == "num_sve_loops":
+output-bmk-results.py(110):     return default_threshold[(change_kind,metric,mode)]
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 481.wrf,libc.so.6 : sample=-10% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+ --- modulename: output-bmk-results, funcname: is_entry_improvement
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -6442,17 +7316,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 481.wrf,[.] powf@@GLIBC_2.27 : sample=1% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 481.wrf,[.] __module_small_step_em_MOD_advance_uv : sample=-12% (threshold=21.18%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -6469,17 +7343,17 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
 DEBUG: checking symbol.improvement : 482.sphinx3,[.] vector_gautbl_eval_logs3 : sample=-2% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(218):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(226):         threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
  --- modulename: output-bmk-results, funcname: get_threshold
 output-bmk-results.py(98):     if metric == "sample":
 output-bmk-results.py(99):        spec_thr = get_specific_thresholds(metric, mode, bmk, symb)
@@ -6496,40 +7370,41 @@ output-bmk-results.py(81):        return threshold
 output-bmk-results.py(100):        if not np.isnan(spec_thr):
 output-bmk-results.py(104):           spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)])
 output-bmk-results.py(105):           return spec_thr
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-output-bmk-results.py(221):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
-output-bmk-results.py(220):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
-DEBUG: checking symbol.improvement : 482.sphinx3,[.] mgau_eval : sample=2% (threshold=15%)
-output-bmk-results.py(224):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+output-bmk-results.py(229):              .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold))
+output-bmk-results.py(228):         print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\
+DEBUG: checking symbol.improvement : 482.sphinx3,[.] mgau_eval : sample=-2% (threshold=15%)
+output-bmk-results.py(232):         if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
  --- modulename: output-bmk-results, funcname: is_entry_improvement
-output-bmk-results.py(184):     if metric in metric_utils.higher_regress_metrics:
-output-bmk-results.py(185):         return (100 - result > threshold)
-output-bmk-results.py(225):             continue
-output-bmk-results.py(216):     for index, row in out_df.iterrows():
-output-bmk-results.py(245):     f_out.close()
+output-bmk-results.py(192):     if metric in metric_utils.higher_regress_metrics:
+output-bmk-results.py(193):         return (100 - result > threshold)
+output-bmk-results.py(233):             continue
+output-bmk-results.py(224):     for index, row in out_df.iterrows():
+output-bmk-results.py(253):     f_out.close()
  --- modulename: output-bmk-results, funcname: close
 output-bmk-results.py(29):     if not self.outf:
 output-bmk-results.py(31):     self.outf.close()
 output-bmk-results.py(32):     if os.stat(self.filename).st_size == 0:
 output-bmk-results.py(33):        os.remove(self.filename)
-output-bmk-results.py(268):     f_ebp.write("\n")
+output-bmk-results.py(303):     f_ebp.write("\n")
  --- modulename: output-bmk-results, funcname: write
 output-bmk-results.py(36):     if not self.predicate or not self.outf:
 output-bmk-results.py(38):     self.outf.write(string)
-output-bmk-results.py(270):     f_skip.close()
+output-bmk-results.py(305):     f_skip.close()
  --- modulename: output-bmk-results, funcname: close
 output-bmk-results.py(29):     if not self.outf:
 output-bmk-results.py(31):     self.outf.close()
 output-bmk-results.py(32):     if os.stat(self.filename).st_size == 0:
-output-bmk-results.py(271):     f_regr.close()
+output-bmk-results.py(33):        os.remove(self.filename)
+output-bmk-results.py(306):     f_regr.close()
  --- modulename: output-bmk-results, funcname: close
 output-bmk-results.py(29):     if not self.outf:
 output-bmk-results.py(31):     self.outf.close()
 output-bmk-results.py(32):     if os.stat(self.filename).st_size == 0:
 output-bmk-results.py(33):        os.remove(self.filename)
-output-bmk-results.py(272):     f_ebp.close()
+output-bmk-results.py(307):     f_ebp.close()
  --- modulename: output-bmk-results, funcname: close
 output-bmk-results.py(29):     if not self.outf:
 output-bmk-results.py(31):     self.outf.close()
 output-bmk-results.py(32):     if os.stat(self.filename).st_size == 0:
-output-bmk-results.py(297):     return 0
+output-bmk-results.py(332):     return 0