diff options
| author | TCWG BuildSlave <tcwg-buildslave@linaro.org> | 2023-11-29 02:26:51 +0000 |
|---|---|---|
| committer | TCWG BuildSlave <tcwg-buildslave@linaro.org> | 2023-11-29 02:27:15 +0000 |
| commit | 12cd058a4bc6f7ab96d8667e5513aabc3a4be7cc (patch) | |
| tree | 2d5430de0a39b0a2b781366a626dd2a89d2367b9 /notify | |
| parent | 36ef4120c58219c2523c81965fcad46a5e5fca2f (diff) | |
onsuccess: #108: 1: [TCWG CI] https://ci.linaro.org/job/tcwg_bmk-code_speed-spec2k6--gnu-arm-master-O3-build/108/
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
Diffstat (limited to 'notify')
| -rw-r--r-- | notify/any.skipped | 4 | ||||
| -rw-r--r-- | notify/exe.improvement | 1 | ||||
| -rw-r--r-- | notify/jira/comment-template.txt | 4 | ||||
| -rw-r--r-- | notify/jira/comments.txt | 4 | ||||
| -rw-r--r-- | notify/lnt_report.json | 328 | ||||
| -rw-r--r-- | notify/mail-body.txt | 44 | ||||
| -rw-r--r-- | notify/mail-subject.txt | 2 | ||||
| -rw-r--r-- | notify/notify-full.log | 305 | ||||
| -rw-r--r-- | notify/notify-init.log | 109 | ||||
| -rw-r--r-- | notify/output-bmk-results.log | 4757 |
10 files changed, 5015 insertions, 543 deletions
diff --git a/notify/any.skipped b/notify/any.skipped new file mode 100644 index 00000000..129b2eb7 --- /dev/null +++ b/notify/any.skipped @@ -0,0 +1,4 @@ +437.leslie3d,leslie3d_base.default,sped up by 17% - 437.leslie3d,sped up by 17% - 437.leslie3d - from 14410 to 11988 perf samples
+437.leslie3d,[.] fluxk_,sped up by 22% - 437.leslie3d:[.] fluxk_,sped up by 22% - 437.leslie3d:[.] fluxk_ - from 3150 to 2448 perf samples
+437.leslie3d,[.] fluxj_,sped up by 24% - 437.leslie3d:[.] fluxj_,sped up by 24% - 437.leslie3d:[.] fluxj_ - from 2990 to 2279 perf samples
+437.leslie3d,[.] fluxi_,sped up by 23% - 437.leslie3d:[.] fluxi_,sped up by 23% - 437.leslie3d:[.] fluxi_ - from 2524 to 1931 perf samples
diff --git a/notify/exe.improvement b/notify/exe.improvement deleted file mode 100644 index 4f7e4fd1..00000000 --- a/notify/exe.improvement +++ /dev/null @@ -1 +0,0 @@ -4,447.dealII,dealII_base.default,sped up by 4% - 447.dealII,sped up by 4% - 447.dealII - from 5808 to 5578 perf samples
diff --git a/notify/jira/comment-template.txt b/notify/jira/comment-template.txt index 088fa2e9..fc456fde 100644 --- a/notify/jira/comment-template.txt +++ b/notify/jira/comment-template.txt @@ -1,3 +1,3 @@ [GNU-689] -sped up by 4% - 447.dealII -Details: https://ci.linaro.org/job/tcwg_bmk-code_speed-spec2k6--gnu-arm-master-O3-build/103/artifact/artifacts/notify/mail-body.txt/*view*/ +No change +Details: https://ci.linaro.org/job/tcwg_bmk-code_speed-spec2k6--gnu-arm-master-O3-build/108/artifact/artifacts/notify/mail-body.txt/*view*/ diff --git a/notify/jira/comments.txt b/notify/jira/comments.txt index ce91af0c..fc456fde 100644 --- a/notify/jira/comments.txt +++ b/notify/jira/comments.txt @@ -1,3 +1,3 @@ [GNU-689] -447.dealII sped up by 4% -Details: https://ci.linaro.org/job/tcwg_bmk-code_speed-spec2k6--gnu-arm-master-O3-build/103/artifact/artifacts/notify/mail-body.txt/*view*/ +No change +Details: https://ci.linaro.org/job/tcwg_bmk-code_speed-spec2k6--gnu-arm-master-O3-build/108/artifact/artifacts/notify/mail-body.txt/*view*/ diff --git a/notify/lnt_report.json b/notify/lnt_report.json index e521089e..c68dc677 100644 --- a/notify/lnt_report.json +++ b/notify/lnt_report.json @@ -7,198 +7,198 @@ "Info": { "tag": "tcwg_bmk-code_speed-spec2k6", "run_order": "basepoints/gcc-14-02623-gc11a3aedec2", - "test_url": "https://ci.linaro.org/job/tcwg_bmk-code_speed-spec2k6--gnu-arm-master-O3-build/103/", - "git_binutils": "https://sourceware.org/git/?p=binutils-gdb.git;a=commit;h=fdc60e8cf680d6eb35813150b85283fc92ec2c0d", + "test_url": "https://ci.linaro.org/job/tcwg_bmk-code_speed-spec2k6--gnu-arm-master-O3-build/108/", + "git_binutils": "https://sourceware.org/git/?p=binutils-gdb.git;a=commit;h=8e72ee1de8df0789c0ac593467d34387af388c83", "git_gcc": "https://github.com/gcc-mirror/gcc/commit/c11a3aedec2649d72d1b7a3a2bd909c5863eefa1", - "git_linux": "https://git.linaro.org/kernel-org/linux-stable.git/commit/?id=3a9c734cf38026353ce3ea9ca342f4ee2cee4ddd", - "git_glibc": "https://sourceware.org/git/?p=glibc.git;a=commit;h=374cab0d95493c65bfcf8b7160a35d00258ff929", + "git_linux": "https://git.linaro.org/kernel-org/linux-stable.git/commit/?id=44ec516fcb54477932225f71261c39cad7f532af", + "git_glibc": "https://sourceware.org/git/?p=glibc.git;a=commit;h=14126ff059e98e9236633741fd323a1116299872", "__report_version__": "1" }, - "Start Time": "2023-07-08 20:08:46" + "Start Time": "2023-07-18 22:04:33" }, "Tests": [ { "Data": [ - 1180353 + 298077 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.400.perlbench.code_size" + "Name": "tcwg_bmk-code_speed-spec2k6.456.hmmer.code_size" } , { "Data": [ - 3214957 + 158008 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.403.gcc.code_size" + "Name": "tcwg_bmk-code_speed-spec2k6.458.sjeng.code_size" } , { "Data": [ - 24622 + 402925 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.410.bwaves.code_size" + "Name": "tcwg_bmk-code_speed-spec2k6.459.GemsFDTD.code_size" } , { "Data": [ - 76304 + 9488654 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.401.bzip2.code_size" + "Name": "tcwg_bmk-code_speed-spec2k6.416.gamess.code_size" } , { "Data": [ - 196751 + 340786 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.482.sphinx3.code_size" + "Name": "tcwg_bmk-code_speed-spec2k6.434.zeusmp.code_size" } , { "Data": [ - 6963762 + 123770 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.481.wrf.code_size" + "Name": "tcwg_bmk-code_speed-spec2k6.433.milc.code_size" } , { "Data": [ - 3777930 + 10603 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.483.xalancbmk.code_size" + "Name": "tcwg_bmk-code_speed-spec2k6.429.mcf.code_size" } , { "Data": [ - 792056 + 1180353 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.464.h264ref.code_size" + "Name": "tcwg_bmk-code_speed-spec2k6.400.perlbench.code_size" } , { "Data": [ - 34759 + 3214957 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.462.libquantum.code_size" + "Name": "tcwg_bmk-code_speed-spec2k6.403.gcc.code_size" } , { "Data": [ - 4319613 + 24622 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.465.tonto.code_size" + "Name": "tcwg_bmk-code_speed-spec2k6.410.bwaves.code_size" } , { "Data": [ - 298077 + 76304 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.456.hmmer.code_size" + "Name": "tcwg_bmk-code_speed-spec2k6.401.bzip2.code_size" } , { "Data": [ - 158008 + 1484877 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.458.sjeng.code_size" + "Name": "tcwg_bmk-code_speed-spec2k6.445.gobmk.code_size" } , { "Data": [ - 402925 + 3122459 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.459.GemsFDTD.code_size" + "Name": "tcwg_bmk-code_speed-spec2k6.447.dealII.code_size" } , { "Data": [ - 9488654 + 230018 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.416.gamess.code_size" + "Name": "tcwg_bmk-code_speed-spec2k6.444.namd.code_size" } , { "Data": [ - 340786 + 1745546 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.434.zeusmp.code_size" + "Name": "tcwg_bmk-code_speed-spec2k6.454.calculix.code_size" } , { "Data": [ - 123770 + 948850 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.433.milc.code_size" + "Name": "tcwg_bmk-code_speed-spec2k6.453.povray.code_size" } , { "Data": [ - 10603 + 409521 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.429.mcf.code_size" + "Name": "tcwg_bmk-code_speed-spec2k6.450.soplex.code_size" } , { "Data": [ - 1484877 + 928949 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.445.gobmk.code_size" + "Name": "tcwg_bmk-code_speed-spec2k6.435.gromacs.code_size" } , { "Data": [ - 3122459 + 741283 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.447.dealII.code_size" + "Name": "tcwg_bmk-code_speed-spec2k6.436.cactusADM.code_size" } , { "Data": [ - 230018 + 102875 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.444.namd.code_size" + "Name": "tcwg_bmk-code_speed-spec2k6.437.leslie3d.code_size" } , { "Data": [ - 1745546 + 792056 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.454.calculix.code_size" + "Name": "tcwg_bmk-code_speed-spec2k6.464.h264ref.code_size" } , { "Data": [ - 948850 + 34759 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.453.povray.code_size" + "Name": "tcwg_bmk-code_speed-spec2k6.462.libquantum.code_size" } , { "Data": [ - 409521 + 4319613 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.450.soplex.code_size" + "Name": "tcwg_bmk-code_speed-spec2k6.465.tonto.code_size" } , { @@ -227,55 +227,55 @@ , { "Data": [ - 928949 + 196751 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.435.gromacs.code_size" + "Name": "tcwg_bmk-code_speed-spec2k6.482.sphinx3.code_size" } , { "Data": [ - 741283 + 6963762 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.436.cactusADM.code_size" + "Name": "tcwg_bmk-code_speed-spec2k6.481.wrf.code_size" } , { "Data": [ - 102875 + 3777930 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.437.leslie3d.code_size" + "Name": "tcwg_bmk-code_speed-spec2k6.483.xalancbmk.code_size" } , { "Data": [ - 7957 + 10003 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.456.hmmer.exec" + "Name": "tcwg_bmk-code_speed-spec2k6.444.namd.exec" } , { "Data": [ - 13353 + 10543 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.458.sjeng.exec" + "Name": "tcwg_bmk-code_speed-spec2k6.445.gobmk.exec" } , { "Data": [ - 13094 + 5555 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.459.GemsFDTD.exec" + "Name": "tcwg_bmk-code_speed-spec2k6.447.dealII.exec" } , { "Data": [ - 13491 + 13437 ], "Info": {}, "Name": "tcwg_bmk-code_speed-spec2k6.470.lbm.exec" @@ -283,7 +283,7 @@ , { "Data": [ - 6827 + 6807 ], "Info": {}, "Name": "tcwg_bmk-code_speed-spec2k6.471.omnetpp.exec" @@ -291,7 +291,7 @@ , { "Data": [ - 8885 + 8886 ], "Info": {}, "Name": "tcwg_bmk-code_speed-spec2k6.473.astar.exec" @@ -299,186 +299,186 @@ , { "Data": [ - 11468 + 21710 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.481.wrf.exec" + "Name": "tcwg_bmk-code_speed-spec2k6.416.gamess.exec" } , { "Data": [ - 25684 + 10837 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.482.sphinx3.exec" + "Name": "tcwg_bmk-code_speed-spec2k6.429.mcf.exec" } , { "Data": [ - 5355 + 10621 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.483.xalancbmk.exec" + "Name": "tcwg_bmk-code_speed-spec2k6.433.milc.exec" } , { "Data": [ - 10007 + 12558 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.444.namd.exec" + "Name": "tcwg_bmk-code_speed-spec2k6.434.zeusmp.exec" } , { "Data": [ - 10547 + 9836 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.445.gobmk.exec" + "Name": "tcwg_bmk-code_speed-spec2k6.435.gromacs.exec" } , { "Data": [ - 5578 + 26811 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.447.dealII.exec" + "Name": "tcwg_bmk-code_speed-spec2k6.436.cactusADM.exec" } , { "Data": [ - 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12517 + 11489 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.434.zeusmp.exec" + "Name": "tcwg_bmk-code_speed-spec2k6.481.wrf.exec" } , { "Data": [ - 9825 + 25687 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.435.gromacs.exec" + "Name": "tcwg_bmk-code_speed-spec2k6.482.sphinx3.exec" } , { "Data": [ - 26787 + 5445 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.436.cactusADM.exec" + "Name": "tcwg_bmk-code_speed-spec2k6.483.xalancbmk.exec" } , { "Data": [ - 14410 + 7948 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.437.leslie3d.exec" + "Name": "tcwg_bmk-code_speed-spec2k6.400.perlbench.exec" } , { "Data": [ - 16150 + 11582 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.462.libquantum.exec" + "Name": "tcwg_bmk-code_speed-spec2k6.401.bzip2.exec" } , { "Data": [ - 9521 + 6288 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.464.h264ref.exec" + "Name": "tcwg_bmk-code_speed-spec2k6.403.gcc.exec" } , { "Data": [ - 6564 + 12849 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.465.tonto.exec" + "Name": "tcwg_bmk-code_speed-spec2k6.410.bwaves.exec" } , { "Data": [ - 7897 + 16109 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.450.soplex.exec" + "Name": "tcwg_bmk-code_speed-spec2k6.462.libquantum.exec" } , { "Data": [ - 4541 + 9523 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.453.povray.exec" + "Name": "tcwg_bmk-code_speed-spec2k6.464.h264ref.exec" } , { "Data": [ - 18324 + 6574 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.454.calculix.exec" + "Name": "tcwg_bmk-code_speed-spec2k6.465.tonto.exec" } , { @@ -486,7 +486,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.456.hmmer.compile_status" + "Name": "tcwg_bmk-code_speed-spec2k6.444.namd.compile_status" } , { @@ -494,7 +494,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.456.hmmer.execution_status" + "Name": "tcwg_bmk-code_speed-spec2k6.444.namd.execution_status" } , { @@ -502,7 +502,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.458.sjeng.compile_status" + "Name": "tcwg_bmk-code_speed-spec2k6.445.gobmk.compile_status" } , { @@ -510,7 +510,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.458.sjeng.execution_status" + "Name": "tcwg_bmk-code_speed-spec2k6.445.gobmk.execution_status" } , { @@ -518,7 +518,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.459.GemsFDTD.compile_status" + "Name": "tcwg_bmk-code_speed-spec2k6.447.dealII.compile_status" } , { @@ -526,7 +526,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.459.GemsFDTD.execution_status" + "Name": "tcwg_bmk-code_speed-spec2k6.447.dealII.execution_status" } , { @@ -582,7 +582,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.481.wrf.compile_status" + "Name": "tcwg_bmk-code_speed-spec2k6.416.gamess.compile_status" } , { @@ -590,7 +590,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.481.wrf.execution_status" + "Name": "tcwg_bmk-code_speed-spec2k6.416.gamess.execution_status" } , { @@ -598,7 +598,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.482.sphinx3.compile_status" + "Name": "tcwg_bmk-code_speed-spec2k6.429.mcf.compile_status" } , { @@ -606,7 +606,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.482.sphinx3.execution_status" + "Name": "tcwg_bmk-code_speed-spec2k6.429.mcf.execution_status" } , { @@ -614,7 +614,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.483.xalancbmk.compile_status" + "Name": "tcwg_bmk-code_speed-spec2k6.433.milc.compile_status" } , { @@ -622,7 +622,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.483.xalancbmk.execution_status" + "Name": "tcwg_bmk-code_speed-spec2k6.433.milc.execution_status" } , { @@ -630,7 +630,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.444.namd.compile_status" + "Name": "tcwg_bmk-code_speed-spec2k6.434.zeusmp.compile_status" } , { @@ -638,7 +638,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.444.namd.execution_status" + "Name": "tcwg_bmk-code_speed-spec2k6.434.zeusmp.execution_status" } , { @@ -646,7 +646,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.445.gobmk.compile_status" + "Name": "tcwg_bmk-code_speed-spec2k6.435.gromacs.compile_status" } , { @@ -654,7 +654,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.445.gobmk.execution_status" + "Name": "tcwg_bmk-code_speed-spec2k6.435.gromacs.execution_status" } , { @@ -662,7 +662,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.447.dealII.compile_status" + "Name": "tcwg_bmk-code_speed-spec2k6.436.cactusADM.compile_status" } , { @@ -670,7 +670,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.447.dealII.execution_status" + "Name": "tcwg_bmk-code_speed-spec2k6.436.cactusADM.execution_status" } , { @@ -678,7 +678,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.400.perlbench.compile_status" + "Name": "tcwg_bmk-code_speed-spec2k6.437.leslie3d.compile_status" } , { @@ -686,7 +686,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.400.perlbench.execution_status" + "Name": "tcwg_bmk-code_speed-spec2k6.437.leslie3d.execution_status" } , { @@ -694,7 +694,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.401.bzip2.compile_status" + "Name": "tcwg_bmk-code_speed-spec2k6.456.hmmer.compile_status" } , { @@ -702,7 +702,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.401.bzip2.execution_status" + "Name": "tcwg_bmk-code_speed-spec2k6.456.hmmer.execution_status" } , { @@ -710,7 +710,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.403.gcc.compile_status" + "Name": "tcwg_bmk-code_speed-spec2k6.458.sjeng.compile_status" } , { @@ -718,7 +718,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.403.gcc.execution_status" + "Name": "tcwg_bmk-code_speed-spec2k6.458.sjeng.execution_status" } , { @@ -726,7 +726,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.410.bwaves.compile_status" + "Name": "tcwg_bmk-code_speed-spec2k6.459.GemsFDTD.compile_status" } , { @@ -734,7 +734,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.410.bwaves.execution_status" + "Name": "tcwg_bmk-code_speed-spec2k6.459.GemsFDTD.execution_status" } , { @@ -742,7 +742,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.416.gamess.compile_status" + "Name": "tcwg_bmk-code_speed-spec2k6.450.soplex.compile_status" } , { @@ -750,7 +750,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.416.gamess.execution_status" + "Name": "tcwg_bmk-code_speed-spec2k6.450.soplex.execution_status" } , { @@ -758,7 +758,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.429.mcf.compile_status" + "Name": "tcwg_bmk-code_speed-spec2k6.453.povray.compile_status" } , { @@ -766,7 +766,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.429.mcf.execution_status" + "Name": "tcwg_bmk-code_speed-spec2k6.453.povray.execution_status" } , { @@ -774,7 +774,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.433.milc.compile_status" + "Name": "tcwg_bmk-code_speed-spec2k6.454.calculix.compile_status" } , { @@ -782,7 +782,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.433.milc.execution_status" + "Name": "tcwg_bmk-code_speed-spec2k6.454.calculix.execution_status" } , { @@ -790,7 +790,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.434.zeusmp.compile_status" + "Name": "tcwg_bmk-code_speed-spec2k6.481.wrf.compile_status" } , { @@ -798,7 +798,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.434.zeusmp.execution_status" + "Name": "tcwg_bmk-code_speed-spec2k6.481.wrf.execution_status" } , { @@ -806,7 +806,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.435.gromacs.compile_status" + "Name": "tcwg_bmk-code_speed-spec2k6.482.sphinx3.compile_status" } , { @@ -814,7 +814,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.435.gromacs.execution_status" + "Name": "tcwg_bmk-code_speed-spec2k6.482.sphinx3.execution_status" } , { @@ -822,7 +822,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.436.cactusADM.compile_status" + "Name": "tcwg_bmk-code_speed-spec2k6.483.xalancbmk.compile_status" } , { @@ -830,7 +830,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.436.cactusADM.execution_status" + "Name": "tcwg_bmk-code_speed-spec2k6.483.xalancbmk.execution_status" } , { @@ -838,7 +838,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.437.leslie3d.compile_status" + "Name": "tcwg_bmk-code_speed-spec2k6.400.perlbench.compile_status" } , { @@ -846,7 +846,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.437.leslie3d.execution_status" + "Name": "tcwg_bmk-code_speed-spec2k6.400.perlbench.execution_status" } , { @@ -854,7 +854,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.462.libquantum.compile_status" + "Name": "tcwg_bmk-code_speed-spec2k6.401.bzip2.compile_status" } , { @@ -862,7 +862,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.462.libquantum.execution_status" + "Name": "tcwg_bmk-code_speed-spec2k6.401.bzip2.execution_status" } , { @@ -870,7 +870,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.464.h264ref.compile_status" + "Name": "tcwg_bmk-code_speed-spec2k6.403.gcc.compile_status" } , { @@ -878,7 +878,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.464.h264ref.execution_status" + "Name": "tcwg_bmk-code_speed-spec2k6.403.gcc.execution_status" } , { @@ -886,7 +886,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.465.tonto.compile_status" + "Name": "tcwg_bmk-code_speed-spec2k6.410.bwaves.compile_status" } , { @@ -894,7 +894,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.465.tonto.execution_status" + "Name": "tcwg_bmk-code_speed-spec2k6.410.bwaves.execution_status" } , { @@ -902,7 +902,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.450.soplex.compile_status" + "Name": "tcwg_bmk-code_speed-spec2k6.462.libquantum.compile_status" } , { @@ -910,7 +910,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.450.soplex.execution_status" + "Name": "tcwg_bmk-code_speed-spec2k6.462.libquantum.execution_status" } , { @@ -918,7 +918,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.453.povray.compile_status" + "Name": "tcwg_bmk-code_speed-spec2k6.464.h264ref.compile_status" } , { @@ -926,7 +926,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.453.povray.execution_status" + "Name": "tcwg_bmk-code_speed-spec2k6.464.h264ref.execution_status" } , { @@ -934,7 +934,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.454.calculix.compile_status" + "Name": "tcwg_bmk-code_speed-spec2k6.465.tonto.compile_status" } , { @@ -942,7 +942,7 @@ 0 ], "Info": {}, - "Name": "tcwg_bmk-code_speed-spec2k6.454.calculix.execution_status" + "Name": "tcwg_bmk-code_speed-spec2k6.465.tonto.execution_status" } ] } diff --git a/notify/mail-body.txt b/notify/mail-body.txt index bf038679..6644a722 100644 --- a/notify/mail-body.txt +++ b/notify/mail-body.txt @@ -2,25 +2,27 @@ 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: - | 162 commits in binutils,gcc,glibc - | fdc60e8cf68 Updated Swedish translation for the binutils subdirectory - | 9cc5af6a1f8 PR 30632 - ld segfaults if linker script includes a STARTUP line. - | b0a101c53a1 RISC-V: Supports Zcb extension. - | 7ab8bf1c777 RISC-V: Support Zca/f/d extensions. - | ffcdd0184d2 Automatic date update in version.in - | ... and 40 more commits in binutils - | c11a3aedec2 tree-ssa-loop-ch improvements, part 3 - | b41a927bcbd c++: constexpr bit_cast with empty field - | b80e3c468e3 [modula2] Uninitialized variable static analysis improvements - | cbe5f6859a7 middle-end/105715 - missed RTL if-conversion with COND_EXPR expansion - | cde17323f95 c++: non-standalone surrogate call template - | ... and 109 more commits in gcc - | 374cab0d95 Regenerate libc.pot - | c6cb8783b5 configure: Use autoconf 2.71 - | 5a70ac9d39 Update sparc libm-test-ulps - -the following benchmarks speeds up by more than 3%: -- sped up by 4% - 447.dealII - from 5808 to 5578 perf samples
+ | 1482 commits in binutils,linux,glibc + | 8e72ee1de8d Automatic date update in version.in + | 648bd020a28 bpf: remove spurious comment from tc-bpf.c + | 249d4715e41 bpf: gas: support relaxation of V4 jump instructions + | 07d8d4bd2ad [gdb] Rename variable main_thread to main_thread_id + | 0c8a0b88d18 Re-acquire GIL earlier in gdbpy_parse_and_eval + | ... and 108 more commits in binutils + | 44ec516fcb54 Merge v6.4.7 + | 4e382c2b4683 Linux 6.4.7 + | 8ab7147dfae7 Revert "drm/amd/display: edp do not add non-edid timings" + | e4f89142977e drm/amd/display: Add polling method to handle MST reply packet + | cae69403a82c drm/amd/display: Clean up errors & warnings in amdgpu_dm.c + | ... and 1324 more commits in linux + | 14126ff059 install.texi: Update versions of most recent build tools + | 1d5355ddbb contrib.texi: Update for 2.38 + | 1547d6a64f <sys/platform/x86.h>: Add APX support + | c8c8dbbf27 translations: update cs, nl, vi + | 784ae96811 string: Fix tester build with fortify enable with gcc 6 + | ... and 35 more commits in glibc + +No change The configuration of this build is: Below reproducer instructions can be used to re-build both "first_bad" and "last_good" cross-toolchains used in this bisection. Naturally, the scripts will fail when triggerring benchmarking jobs if you don\'t have access to Linaro TCWG CI. @@ -38,6 +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/103/artifact/artifacts -Reference build : https://ci.linaro.org/job/tcwg_bmk-code_speed-spec2k6--gnu-arm-master-O3-build/102/artifact/artifacts +Current build : https://ci.linaro.org/job/tcwg_bmk-code_speed-spec2k6--gnu-arm-master-O3-build/108/artifact/artifacts +Reference build : https://ci.linaro.org/job/tcwg_bmk-code_speed-spec2k6--gnu-arm-master-O3-build/103/artifact/artifacts diff --git a/notify/mail-subject.txt b/notify/mail-subject.txt index 6c356973..eda8d43c 100644 --- a/notify/mail-subject.txt +++ b/notify/mail-subject.txt @@ -1 +1 @@ -[Linaro-TCWG-CI] 162 commits in binutils,gcc,glibc: sped up by 4% - 447.dealII on arm O3 +[Linaro-TCWG-CI] 1482 commits in binutils,linux,glibc: No change on arm O3 diff --git a/notify/notify-full.log b/notify/notify-full.log index c0b74d6e..6009b631 100644 --- a/notify/notify-full.log +++ b/notify/notify-full.log @@ -1,6 +1,6 @@ -MOUNTS: /home/tcwg-buildslave/workspace/tcwg_bmk_1:/home/tcwg-buildslave/workspace/tcwg_bmk_1 /home/tcwg-buildslave/snapshots-ref:/home/tcwg-buildslave/snapshots-ref:ro /etc/ssh/ssh_host_dsa_key.pub:/etc/ssh/ssh_host_dsa_key.pub:ro /etc/ssh/ssh_host_rsa_key:/etc/ssh/ssh_host_rsa_key:ro /etc/ssh/ssh_host_ecdsa_key:/etc/ssh/ssh_host_ecdsa_key:ro /etc/ssh/ssh_host_ed25519_key.pub:/etc/ssh/ssh_host_ed25519_key.pub:ro /etc/ssh/ssh_host_dsa_key:/etc/ssh/ssh_host_dsa_key:ro /etc/ssh/ssh_host_ecdsa_key.pub:/etc/ssh/ssh_host_ecdsa_key.pub:ro /etc/ssh/ssh_host_ed25519_key:/etc/ssh/ssh_host_ed25519_key:ro /etc/ssh/ssh_host_rsa_key.pub:/etc/ssh/ssh_host_rsa_key.pub:ro ccache-tcwg_bmk-amd64-jammy:/home/tcwg-buildslave/.ccache /home/tcwg-benchmark -DEBUG: starting docker on build-10.tcwglab from build-10, date Tue Jul 18 06:53:18 PM UTC 2023 -ssh -Snone -oForwardAgent=no build-10.tcwglab docker-wrapper run --name 103-tcwg_bmk-code_speed-spec2k6--gnu-arm-master-O3-build -dtP -v /home/tcwg-buildslave/workspace/tcwg_bmk_1:/home/tcwg-buildslave/workspace/tcwg_bmk_1 -v /home/tcwg-buildslave/snapshots-ref:/home/tcwg-buildslave/snapshots-ref:ro -v /etc/ssh/ssh_host_dsa_key.pub:/etc/ssh/ssh_host_dsa_key.pub:ro -v /etc/ssh/ssh_host_rsa_key:/etc/ssh/ssh_host_rsa_key:ro -v /etc/ssh/ssh_host_ecdsa_key:/etc/ssh/ssh_host_ecdsa_key:ro -v /etc/ssh/ssh_host_ed25519_key.pub:/etc/ssh/ssh_host_ed25519_key.pub:ro -v /etc/ssh/ssh_host_dsa_key:/etc/ssh/ssh_host_dsa_key:ro -v /etc/ssh/ssh_host_ecdsa_key.pub:/etc/ssh/ssh_host_ecdsa_key.pub:ro -v /etc/ssh/ssh_host_ed25519_key:/etc/ssh/ssh_host_ed25519_key:ro -v /etc/ssh/ssh_host_rsa_key.pub:/etc/ssh/ssh_host_rsa_key.pub:ro -v ccache-tcwg_bmk-amd64-jammy:/home/tcwg-buildslave/.ccache -v /home/tcwg-benchmark --cap-add=SYS_PTRACE --security-opt seccomp:unconfined --memory=16000M --pids-limit=5000 --cpu-shares=1000 --cpuset-cpus 0-7 linaro/ci-amd64-tcwg-build-ubuntu:jammy +MOUNTS: /home/tcwg-buildslave/workspace/tcwg_bmk_1:/home/tcwg-buildslave/workspace/tcwg_bmk_1 /home/tcwg-buildslave/snapshots-ref:/home/tcwg-buildslave/snapshots-ref:ro /etc/ssh/ssh_host_ed25519_key:/etc/ssh/ssh_host_ed25519_key:ro /etc/ssh/ssh_host_dsa_key:/etc/ssh/ssh_host_dsa_key:ro /etc/ssh/ssh_host_ed25519_key.pub:/etc/ssh/ssh_host_ed25519_key.pub:ro /etc/ssh/ssh_host_rsa_key.pub:/etc/ssh/ssh_host_rsa_key.pub:ro /etc/ssh/ssh_host_dsa_key.pub:/etc/ssh/ssh_host_dsa_key.pub:ro /etc/ssh/ssh_host_ecdsa_key:/etc/ssh/ssh_host_ecdsa_key:ro /etc/ssh/ssh_host_ecdsa_key.pub:/etc/ssh/ssh_host_ecdsa_key.pub:ro /etc/ssh/ssh_host_rsa_key:/etc/ssh/ssh_host_rsa_key:ro ccache-tcwg_bmk-amd64-jammy:/home/tcwg-buildslave/.ccache /home/tcwg-benchmark +DEBUG: starting docker on dev-01.tcwglab from dev-01, date Sun Jul 30 09:14:04 AM UTC 2023 +ssh -Snone -oForwardAgent=no dev-01.tcwglab docker-wrapper run --name 108-tcwg_bmk-code_speed-spec2k6--gnu-arm-master-O3-build -dtP -v /home/tcwg-buildslave/workspace/tcwg_bmk_1:/home/tcwg-buildslave/workspace/tcwg_bmk_1 -v /home/tcwg-buildslave/snapshots-ref:/home/tcwg-buildslave/snapshots-ref:ro -v /etc/ssh/ssh_host_ed25519_key:/etc/ssh/ssh_host_ed25519_key:ro -v /etc/ssh/ssh_host_dsa_key:/etc/ssh/ssh_host_dsa_key:ro -v /etc/ssh/ssh_host_ed25519_key.pub:/etc/ssh/ssh_host_ed25519_key.pub:ro -v /etc/ssh/ssh_host_rsa_key.pub:/etc/ssh/ssh_host_rsa_key.pub:ro -v /etc/ssh/ssh_host_dsa_key.pub:/etc/ssh/ssh_host_dsa_key.pub:ro -v /etc/ssh/ssh_host_ecdsa_key:/etc/ssh/ssh_host_ecdsa_key:ro -v /etc/ssh/ssh_host_ecdsa_key.pub:/etc/ssh/ssh_host_ecdsa_key.pub:ro -v /etc/ssh/ssh_host_rsa_key:/etc/ssh/ssh_host_rsa_key:ro -v ccache-tcwg_bmk-amd64-jammy:/home/tcwg-buildslave/.ccache -v /home/tcwg-benchmark --memory=64000M --pids-limit=5000 --cpu-shares=1000 --cpuset-cpus 0-31 --cap-add=SYS_PTRACE --security-opt seccomp:unconfined linaro/ci-amd64-tcwg-build-ubuntu:jammy WARNING: Your kernel does not support swap limit capabilities or the cgroup is not mounted. Memory limited without swap. /home/tcwg-buildslave/workspace/tcwg_bmk_1/jenkins-scripts/round-robin-notify.sh @@rr[top_artifacts] artifacts __TCWG_JIRA_TOKEN ijQW9spm0p7HwZnUtLFx7CCA __stage full __verbose true @@ -21,25 +21,25 @@ WARNING: Your kernel does not support swap limit capabilities or the cgroup is n ++ set +x + ci_project=tcwg_bmk-code_speed-spec2k6 ++ get_current_manifest '{rr[ci_config]}' -# Debug traces : ++ get_manifest artifacts/manifest.sh '{rr[ci_config]}' ++ set +x +# Debug traces : + ci_config=gnu-arm-master-O3 + echo '# Debug traces :' ++ get_baseline_manifest BUILD_URL ++ get_manifest base-artifacts/manifest.sh BUILD_URL false ++ set +x -# Baseline : https://ci.linaro.org/job/tcwg_bmk-code_speed-spec2k6--gnu-arm-master-O3-build/102/ +# Baseline : https://ci.linaro.org/job/tcwg_bmk-code_speed-spec2k6--gnu-arm-master-O3-build/103/ # Using dir : base-artifacts -+ echo '# Baseline : https://ci.linaro.org/job/tcwg_bmk-code_speed-spec2k6--gnu-arm-master-O3-build/102/' ++ echo '# Baseline : https://ci.linaro.org/job/tcwg_bmk-code_speed-spec2k6--gnu-arm-master-O3-build/103/' + echo '# Using dir : base-artifacts' ++ get_current_manifest BUILD_URL ++ get_manifest artifacts/manifest.sh BUILD_URL ++ set +x -# Artifacts : https://ci.linaro.org/job/tcwg_bmk-code_speed-spec2k6--gnu-arm-master-O3-build/103/ +# Artifacts : https://ci.linaro.org/job/tcwg_bmk-code_speed-spec2k6--gnu-arm-master-O3-build/108/ # Using dir : artifacts -+ echo '# Artifacts : https://ci.linaro.org/job/tcwg_bmk-code_speed-spec2k6--gnu-arm-master-O3-build/103/' ++ echo '# Artifacts : https://ci.linaro.org/job/tcwg_bmk-code_speed-spec2k6--gnu-arm-master-O3-build/108/' + echo '# Using dir : artifacts' + echo '' + mkdir -p artifacts/notify @@ -61,19 +61,17 @@ WARNING: Your kernel does not support swap limit capabilities or the cgroup is n +++ set -euf -o pipefail +++ local c delim= +++ for c in ${rr[components]} -+++ '[' xgit://sourceware.org/git/binutils-gdb.git#fdc60e8cf680d6eb35813150b85283fc92ec2c0d '!=' xbaseline ']' ++++ '[' xgit://sourceware.org/git/binutils-gdb.git#8e72ee1de8df0789c0ac593467d34387af388c83 '!=' xbaseline ']' +++ echo -ne binutils +++ delim=' ' +++ for c in ${rr[components]} -+++ '[' xhttps://github.com/gcc-mirror/gcc.git#c11a3aedec2649d72d1b7a3a2bd909c5863eefa1 '!=' xbaseline ']' -+++ echo -ne ' gcc' -+++ delim=' ' ++++ '[' xbaseline '!=' xbaseline ']' +++ for c in ${rr[components]} -+++ '[' xhttps://git.linaro.org/kernel-org/linux-stable.git#3a9c734cf38026353ce3ea9ca342f4ee2cee4ddd '!=' xbaseline ']' ++++ '[' xhttps://git.linaro.org/kernel-org/linux-stable.git#44ec516fcb54477932225f71261c39cad7f532af '!=' xbaseline ']' +++ echo -ne ' linux' +++ delim=' ' +++ for c in ${rr[components]} -+++ '[' xgit://sourceware.org/git/glibc.git#374cab0d95493c65bfcf8b7160a35d00258ff929 '!=' xbaseline ']' ++++ '[' xgit://sourceware.org/git/glibc.git#14126ff059e98e9236633741fd323a1116299872 '!=' xbaseline ']' +++ echo -ne ' glibc' +++ delim=' ' +++ echo @@ -89,25 +87,10 @@ WARNING: Your kernel does not support swap limit capabilities or the cgroup is n +++ assert_with_msg 'ERROR: No binutils_rev in baseline git' '[' -f base-artifacts/git/binutils_rev ']' +++ set -euf -o pipefail +x +++ cat base-artifacts/git/binutils_rev -++ '[' xfdc60e8cf680d6eb35813150b85283fc92ec2c0d '!=' x0d8de8f255d3333c7a122c419f7f75f3c6c45df5 ']' +++ '[' x8e72ee1de8df0789c0ac593467d34387af388c83 '!=' xfdc60e8cf680d6eb35813150b85283fc92ec2c0d ']' ++ echo -ne binutils ++ delim=' ' ++ for c in $(print_updated_components) -+++ get_current_git gcc_rev -+++ set -euf -o pipefail -+++ assert_with_msg 'ERROR: No gcc_rev in current git' '[' -f artifacts/git/gcc_rev ']' -+++ set -euf -o pipefail +x -+++ cat artifacts/git/gcc_rev -+++ get_baseline_git gcc_rev -+++ set -euf -o pipefail -+++ local base_artifacts=base-artifacts -+++ assert_with_msg 'ERROR: No gcc_rev in baseline git' '[' -f base-artifacts/git/gcc_rev ']' -+++ set -euf -o pipefail +x -+++ cat base-artifacts/git/gcc_rev -++ '[' xc11a3aedec2649d72d1b7a3a2bd909c5863eefa1 '!=' x8f1a26ee259f72e42405b9c5f2b161042ec4014b ']' -++ echo -ne ' gcc' -++ delim=' ' -++ for c in $(print_updated_components) +++ get_current_git linux_rev +++ set -euf -o pipefail +++ assert_with_msg 'ERROR: No linux_rev in current git' '[' -f artifacts/git/linux_rev ']' @@ -119,7 +102,9 @@ WARNING: Your kernel does not support swap limit capabilities or the cgroup is n +++ assert_with_msg 'ERROR: No linux_rev in baseline git' '[' -f base-artifacts/git/linux_rev ']' +++ set -euf -o pipefail +x +++ cat base-artifacts/git/linux_rev -++ '[' x3a9c734cf38026353ce3ea9ca342f4ee2cee4ddd '!=' x3a9c734cf38026353ce3ea9ca342f4ee2cee4ddd ']' +++ '[' x44ec516fcb54477932225f71261c39cad7f532af '!=' x3a9c734cf38026353ce3ea9ca342f4ee2cee4ddd ']' +++ echo -ne ' linux' +++ delim=' ' ++ for c in $(print_updated_components) +++ get_current_git glibc_rev +++ set -euf -o pipefail @@ -132,19 +117,19 @@ WARNING: Your kernel does not support swap limit capabilities or the cgroup is n +++ assert_with_msg 'ERROR: No glibc_rev in baseline git' '[' -f base-artifacts/git/glibc_rev ']' +++ set -euf -o pipefail +x +++ cat base-artifacts/git/glibc_rev -++ '[' x374cab0d95493c65bfcf8b7160a35d00258ff929 '!=' x721f30116ce653fffb0156e1298c8063833396e3 ']' +++ '[' x14126ff059e98e9236633741fd323a1116299872 '!=' x374cab0d95493c65bfcf8b7160a35d00258ff929 ']' ++ echo -ne ' glibc' ++ delim=' ' ++ echo # Debug traces : -# change_kind=multiple_components : binutils gcc glibc +# change_kind=multiple_components : binutils linux glibc + local c base_rev cur_rev c_commits + '[' 3 = 0 ']' + '[' 3 = 1 ']' + change_kind=multiple_components + changed_single_component= + echo '# Debug traces :' -+ echo '# change_kind=multiple_components : binutils gcc glibc' ++ echo '# change_kind=multiple_components : binutils linux glibc' + for c in "${changed_components[@]}" ++ get_baseline_git binutils_rev ++ set -euf -o pipefail @@ -152,35 +137,35 @@ WARNING: Your kernel does not support swap limit capabilities or the cgroup is n ++ assert_with_msg 'ERROR: No binutils_rev in baseline git' '[' -f base-artifacts/git/binutils_rev ']' ++ set -euf -o pipefail +x ++ cat base-artifacts/git/binutils_rev -+ base_rev=0d8de8f255d3333c7a122c419f7f75f3c6c45df5 ++ base_rev=fdc60e8cf680d6eb35813150b85283fc92ec2c0d ++ get_current_git binutils_rev ++ set -euf -o pipefail ++ assert_with_msg 'ERROR: No binutils_rev in current git' '[' -f artifacts/git/binutils_rev ']' ++ set -euf -o pipefail +x ++ cat artifacts/git/binutils_rev -+ cur_rev=fdc60e8cf680d6eb35813150b85283fc92ec2c0d -++ git -C binutils rev-list --count 0d8de8f255d3333c7a122c419f7f75f3c6c45df5..fdc60e8cf680d6eb35813150b85283fc92ec2c0d -# rev for binutils : 0d8de8f255d3333c7a122c419f7f75f3c6c45df5..fdc60e8cf680d6eb35813150b85283fc92ec2c0d (45 commits) -+ c_commits=45 -+ echo '# rev for binutils : 0d8de8f255d3333c7a122c419f7f75f3c6c45df5..fdc60e8cf680d6eb35813150b85283fc92ec2c0d (45 commits)' ++ cur_rev=8e72ee1de8df0789c0ac593467d34387af388c83 +++ git -C binutils rev-list --count fdc60e8cf680d6eb35813150b85283fc92ec2c0d..8e72ee1de8df0789c0ac593467d34387af388c83 +# rev for binutils : fdc60e8cf680d6eb35813150b85283fc92ec2c0d..8e72ee1de8df0789c0ac593467d34387af388c83 (113 commits) ++ c_commits=113 ++ echo '# rev for binutils : fdc60e8cf680d6eb35813150b85283fc92ec2c0d..8e72ee1de8df0789c0ac593467d34387af388c83 (113 commits)' + for c in "${changed_components[@]}" -++ get_baseline_git gcc_rev +++ get_baseline_git linux_rev ++ set -euf -o pipefail ++ local base_artifacts=base-artifacts -++ assert_with_msg 'ERROR: No gcc_rev in baseline git' '[' -f base-artifacts/git/gcc_rev ']' +++ assert_with_msg 'ERROR: No linux_rev in baseline git' '[' -f base-artifacts/git/linux_rev ']' ++ set -euf -o pipefail +x -++ cat base-artifacts/git/gcc_rev -+ base_rev=8f1a26ee259f72e42405b9c5f2b161042ec4014b -++ get_current_git gcc_rev +++ cat base-artifacts/git/linux_rev ++ base_rev=3a9c734cf38026353ce3ea9ca342f4ee2cee4ddd +++ get_current_git linux_rev ++ set -euf -o pipefail -++ assert_with_msg 'ERROR: No gcc_rev in current git' '[' -f artifacts/git/gcc_rev ']' +++ assert_with_msg 'ERROR: No linux_rev in current git' '[' -f artifacts/git/linux_rev ']' ++ set -euf -o pipefail +x -++ cat artifacts/git/gcc_rev -+ cur_rev=c11a3aedec2649d72d1b7a3a2bd909c5863eefa1 -++ git -C gcc rev-list --count 8f1a26ee259f72e42405b9c5f2b161042ec4014b..c11a3aedec2649d72d1b7a3a2bd909c5863eefa1 -# rev for gcc : 8f1a26ee259f72e42405b9c5f2b161042ec4014b..c11a3aedec2649d72d1b7a3a2bd909c5863eefa1 (114 commits) -+ c_commits=114 -+ echo '# rev for gcc : 8f1a26ee259f72e42405b9c5f2b161042ec4014b..c11a3aedec2649d72d1b7a3a2bd909c5863eefa1 (114 commits)' +++ cat artifacts/git/linux_rev ++ cur_rev=44ec516fcb54477932225f71261c39cad7f532af +++ git -C linux rev-list --count 3a9c734cf38026353ce3ea9ca342f4ee2cee4ddd..44ec516fcb54477932225f71261c39cad7f532af +# rev for linux : 3a9c734cf38026353ce3ea9ca342f4ee2cee4ddd..44ec516fcb54477932225f71261c39cad7f532af (1329 commits) ++ c_commits=1329 ++ echo '# rev for linux : 3a9c734cf38026353ce3ea9ca342f4ee2cee4ddd..44ec516fcb54477932225f71261c39cad7f532af (1329 commits)' + for c in "${changed_components[@]}" ++ get_baseline_git glibc_rev ++ set -euf -o pipefail @@ -188,18 +173,18 @@ WARNING: Your kernel does not support swap limit capabilities or the cgroup is n ++ assert_with_msg 'ERROR: No glibc_rev in baseline git' '[' -f base-artifacts/git/glibc_rev ']' ++ set -euf -o pipefail +x ++ cat base-artifacts/git/glibc_rev -+ base_rev=721f30116ce653fffb0156e1298c8063833396e3 ++ base_rev=374cab0d95493c65bfcf8b7160a35d00258ff929 ++ get_current_git glibc_rev ++ set -euf -o pipefail ++ assert_with_msg 'ERROR: No glibc_rev in current git' '[' -f artifacts/git/glibc_rev ']' ++ set -euf -o pipefail +x ++ cat artifacts/git/glibc_rev -+ cur_rev=374cab0d95493c65bfcf8b7160a35d00258ff929 -++ git -C glibc rev-list --count 721f30116ce653fffb0156e1298c8063833396e3..374cab0d95493c65bfcf8b7160a35d00258ff929 -# rev for glibc : 721f30116ce653fffb0156e1298c8063833396e3..374cab0d95493c65bfcf8b7160a35d00258ff929 (3 commits) ++ cur_rev=14126ff059e98e9236633741fd323a1116299872 +++ git -C glibc rev-list --count 374cab0d95493c65bfcf8b7160a35d00258ff929..14126ff059e98e9236633741fd323a1116299872 +# rev for glibc : 374cab0d95493c65bfcf8b7160a35d00258ff929..14126ff059e98e9236633741fd323a1116299872 (40 commits) -+ c_commits=3 -+ echo '# rev for glibc : 721f30116ce653fffb0156e1298c8063833396e3..374cab0d95493c65bfcf8b7160a35d00258ff929 (3 commits)' ++ c_commits=40 ++ echo '# rev for glibc : 374cab0d95493c65bfcf8b7160a35d00258ff929..14126ff059e98e9236633741fd323a1116299872 (40 commits)' + echo '' + setup_stages_to_run + '[' xignore == xignore ']' @@ -234,8 +219,8 @@ WARNING: Your kernel does not support swap limit capabilities or the cgroup is n + '[' xmultiple_components '!=' xsingle_commit ']' + return + post_interesting_commits full -+ set -euf -o pipefail # post_interesting_commits ++ set -euf -o pipefail + echo '# post_interesting_commits' + local stage=full + '[' multiple_components '!=' single_commit ']' @@ -335,11 +320,11 @@ WARNING: Your kernel does not support swap limit capabilities or the cgroup is n ++ get_current_manifest BUILD_URL ++ get_manifest artifacts/manifest.sh BUILD_URL ++ set +x -+ bad_artifacts_url=https://ci.linaro.org/job/tcwg_bmk-code_speed-spec2k6--gnu-arm-master-O3-build/103/artifact/artifacts ++ bad_artifacts_url=https://ci.linaro.org/job/tcwg_bmk-code_speed-spec2k6--gnu-arm-master-O3-build/108/artifact/artifacts ++ get_baseline_manifest BUILD_URL ++ get_manifest base-artifacts/manifest.sh BUILD_URL false ++ set +x -+ good_artifacts_url=https://ci.linaro.org/job/tcwg_bmk-code_speed-spec2k6--gnu-arm-master-O3-build/102/artifact/artifacts ++ good_artifacts_url=https://ci.linaro.org/job/tcwg_bmk-code_speed-spec2k6--gnu-arm-master-O3-build/103/artifact/artifacts + cat ++ bmk_print_result --oneline ++ false @@ -385,33 +370,33 @@ WARNING: Your kernel does not support swap limit capabilities or the cgroup is n +++ assert_with_msg 'ERROR: No binutils_rev in baseline git' '[' -f base-artifacts/git/binutils_rev ']' +++ set -euf -o pipefail +x +++ cat base-artifacts/git/binutils_rev -++ base_rev=0d8de8f255d3333c7a122c419f7f75f3c6c45df5 +++ base_rev=fdc60e8cf680d6eb35813150b85283fc92ec2c0d +++ get_current_git binutils_rev +++ set -euf -o pipefail +++ assert_with_msg 'ERROR: No binutils_rev in current git' '[' -f artifacts/git/binutils_rev ']' +++ set -euf -o pipefail +x +++ cat artifacts/git/binutils_rev -++ cur_rev=fdc60e8cf680d6eb35813150b85283fc92ec2c0d -+++ git -C binutils rev-list --count 0d8de8f255d3333c7a122c419f7f75f3c6c45df5..fdc60e8cf680d6eb35813150b85283fc92ec2c0d -++ c_commits=45 -++ new_commits=45 +++ cur_rev=8e72ee1de8df0789c0ac593467d34387af388c83 ++++ git -C binutils rev-list --count fdc60e8cf680d6eb35813150b85283fc92ec2c0d..8e72ee1de8df0789c0ac593467d34387af388c83 +++ c_commits=113 +++ new_commits=113 ++ for c in "${changed_components[@]}" -+++ get_baseline_git gcc_rev ++++ get_baseline_git linux_rev +++ set -euf -o pipefail +++ local base_artifacts=base-artifacts -+++ assert_with_msg 'ERROR: No gcc_rev in baseline git' '[' -f base-artifacts/git/gcc_rev ']' ++++ assert_with_msg 'ERROR: No linux_rev in baseline git' '[' -f base-artifacts/git/linux_rev ']' +++ set -euf -o pipefail +x -+++ cat base-artifacts/git/gcc_rev -++ base_rev=8f1a26ee259f72e42405b9c5f2b161042ec4014b -+++ get_current_git gcc_rev ++++ cat base-artifacts/git/linux_rev +++ base_rev=3a9c734cf38026353ce3ea9ca342f4ee2cee4ddd ++++ get_current_git linux_rev +++ set -euf -o pipefail -+++ assert_with_msg 'ERROR: No gcc_rev in current git' '[' -f artifacts/git/gcc_rev ']' ++++ assert_with_msg 'ERROR: No linux_rev in current git' '[' -f artifacts/git/linux_rev ']' +++ set -euf -o pipefail +x -+++ cat artifacts/git/gcc_rev -++ cur_rev=c11a3aedec2649d72d1b7a3a2bd909c5863eefa1 -+++ git -C gcc rev-list --count 8f1a26ee259f72e42405b9c5f2b161042ec4014b..c11a3aedec2649d72d1b7a3a2bd909c5863eefa1 -++ c_commits=114 -++ new_commits=159 ++++ cat artifacts/git/linux_rev +++ cur_rev=44ec516fcb54477932225f71261c39cad7f532af ++++ git -C linux rev-list --count 3a9c734cf38026353ce3ea9ca342f4ee2cee4ddd..44ec516fcb54477932225f71261c39cad7f532af +++ c_commits=1329 +++ new_commits=1442 ++ for c in "${changed_components[@]}" +++ get_baseline_git glibc_rev +++ set -euf -o pipefail @@ -419,25 +404,25 @@ WARNING: Your kernel does not support swap limit capabilities or the cgroup is n +++ assert_with_msg 'ERROR: No glibc_rev in baseline git' '[' -f base-artifacts/git/glibc_rev ']' +++ set -euf -o pipefail +x +++ cat base-artifacts/git/glibc_rev -++ base_rev=721f30116ce653fffb0156e1298c8063833396e3 +++ base_rev=374cab0d95493c65bfcf8b7160a35d00258ff929 +++ get_current_git glibc_rev +++ set -euf -o pipefail +++ assert_with_msg 'ERROR: No glibc_rev in current git' '[' -f artifacts/git/glibc_rev ']' +++ set -euf -o pipefail +x +++ cat artifacts/git/glibc_rev -++ cur_rev=374cab0d95493c65bfcf8b7160a35d00258ff929 -+++ git -C glibc rev-list --count 721f30116ce653fffb0156e1298c8063833396e3..374cab0d95493c65bfcf8b7160a35d00258ff929 -++ c_commits=3 -++ new_commits=162 -+++ echo binutils gcc glibc +++ cur_rev=14126ff059e98e9236633741fd323a1116299872 ++++ git -C glibc rev-list --count 374cab0d95493c65bfcf8b7160a35d00258ff929..14126ff059e98e9236633741fd323a1116299872 +++ c_commits=40 +++ new_commits=1482 ++++ echo binutils linux glibc +++ tr ' ' , -++ components=binutils,gcc,glibc -++ echo '162 commits in binutils,gcc,glibc' -++ sed -e 's/^/ | /' +++ components=binutils,linux,glibc +++ echo '1482 commits in binutils,linux,glibc' ++ print_commits --short ++ false ++ local print_arg=--short ++ local components new_commits more_lines +++ sed -e 's/^/ | /' ++ case "$change_kind:$print_arg" in ++ new_commits=0 ++ for c in "${changed_components[@]}" @@ -447,55 +432,55 @@ WARNING: Your kernel does not support swap limit capabilities or the cgroup is n +++ assert_with_msg 'ERROR: No binutils_rev in baseline git' '[' -f base-artifacts/git/binutils_rev ']' +++ set -euf -o pipefail +x +++ cat base-artifacts/git/binutils_rev -++ base_rev=0d8de8f255d3333c7a122c419f7f75f3c6c45df5 +++ base_rev=fdc60e8cf680d6eb35813150b85283fc92ec2c0d +++ get_current_git binutils_rev +++ set -euf -o pipefail +++ assert_with_msg 'ERROR: No binutils_rev in current git' '[' -f artifacts/git/binutils_rev ']' +++ set -euf -o pipefail +x +++ cat artifacts/git/binutils_rev -++ cur_rev=fdc60e8cf680d6eb35813150b85283fc92ec2c0d -+++ git -C binutils rev-list --count 0d8de8f255d3333c7a122c419f7f75f3c6c45df5..fdc60e8cf680d6eb35813150b85283fc92ec2c0d -++ c_commits=45 -++ new_commits=45 +++ cur_rev=8e72ee1de8df0789c0ac593467d34387af388c83 ++++ git -C binutils rev-list --count fdc60e8cf680d6eb35813150b85283fc92ec2c0d..8e72ee1de8df0789c0ac593467d34387af388c83 +++ c_commits=113 +++ new_commits=113 ++ echo 'binutils commits:' -+++ git -C binutils log --pretty=oneline 0d8de8f255d3333c7a122c419f7f75f3c6c45df5..fdc60e8cf680d6eb35813150b85283fc92ec2c0d ++++ git -C binutils log --pretty=oneline fdc60e8cf680d6eb35813150b85283fc92ec2c0d..8e72ee1de8df0789c0ac593467d34387af388c83 +++ head -n5 +++ true -++ echo 'fdc60e8cf680d6eb35813150b85283fc92ec2c0d Updated Swedish translation for the binutils subdirectory -9cc5af6a1f80ea46b2444e266c69193c32c13d3b PR 30632 - ld segfaults if linker script includes a STARTUP line. -b0a101c53a1caa3f2a9fc2032f703ca4465cfbbb RISC-V: Supports Zcb extension. -7ab8bf1c777644f834ccbc5d1e83d721859ca1ba RISC-V: Support Zca/f/d extensions. -ffcdd0184d2da860d45ab326dd7afe00c432e428 Automatic date update in version.in' -++ '[' 45 -gt 5 ']' -++ echo '... and 40 more' +++ echo '8e72ee1de8df0789c0ac593467d34387af388c83 Automatic date update in version.in +648bd020a28beab70768d44d256b7f5483746d38 bpf: remove spurious comment from tc-bpf.c +249d4715e41061b6bd2d26df20ae274e6478f972 bpf: gas: support relaxation of V4 jump instructions +07d8d4bd2ad213281be502d6e56c19e0269b8967 [gdb] Rename variable main_thread to main_thread_id +0c8a0b88d18d9c8d6cd52bd1a56d6ab88570f287 Re-acquire GIL earlier in gdbpy_parse_and_eval' +++ '[' 113 -gt 5 ']' +++ echo '... and 108 more' ++ for c in "${changed_components[@]}" -+++ get_baseline_git gcc_rev ++++ get_baseline_git linux_rev +++ set -euf -o pipefail +++ local base_artifacts=base-artifacts -+++ assert_with_msg 'ERROR: No gcc_rev in baseline git' '[' -f base-artifacts/git/gcc_rev ']' ++++ assert_with_msg 'ERROR: No linux_rev in baseline git' '[' -f base-artifacts/git/linux_rev ']' +++ set -euf -o pipefail +x -+++ cat base-artifacts/git/gcc_rev -++ base_rev=8f1a26ee259f72e42405b9c5f2b161042ec4014b -+++ get_current_git gcc_rev ++++ cat base-artifacts/git/linux_rev +++ base_rev=3a9c734cf38026353ce3ea9ca342f4ee2cee4ddd ++++ get_current_git linux_rev +++ set -euf -o pipefail -+++ assert_with_msg 'ERROR: No gcc_rev in current git' '[' -f artifacts/git/gcc_rev ']' ++++ assert_with_msg 'ERROR: No linux_rev in current git' '[' -f artifacts/git/linux_rev ']' +++ set -euf -o pipefail +x -+++ cat artifacts/git/gcc_rev -++ cur_rev=c11a3aedec2649d72d1b7a3a2bd909c5863eefa1 -+++ git -C gcc rev-list --count 8f1a26ee259f72e42405b9c5f2b161042ec4014b..c11a3aedec2649d72d1b7a3a2bd909c5863eefa1 -++ c_commits=114 -++ new_commits=159 -++ echo 'gcc commits:' -+++ git -C gcc log --pretty=oneline 8f1a26ee259f72e42405b9c5f2b161042ec4014b..c11a3aedec2649d72d1b7a3a2bd909c5863eefa1 ++++ cat artifacts/git/linux_rev +++ cur_rev=44ec516fcb54477932225f71261c39cad7f532af ++++ git -C linux rev-list --count 3a9c734cf38026353ce3ea9ca342f4ee2cee4ddd..44ec516fcb54477932225f71261c39cad7f532af +++ c_commits=1329 +++ new_commits=1442 +++ echo 'linux commits:' ++++ git -C linux log --pretty=oneline 3a9c734cf38026353ce3ea9ca342f4ee2cee4ddd..44ec516fcb54477932225f71261c39cad7f532af +++ head -n5 +++ true -++ echo 'c11a3aedec2649d72d1b7a3a2bd909c5863eefa1 tree-ssa-loop-ch improvements, part 3 -b41a927bcbdf27723b9d420f0b403f2af12129f1 c++: constexpr bit_cast with empty field -b80e3c468e373cc6fd4e41a5879dbca95a40ac8c [modula2] Uninitialized variable static analysis improvements -cbe5f6859a73b2acf203bd7d13f9fb245d63cbd4 middle-end/105715 - missed RTL if-conversion with COND_EXPR expansion -cde17323f950ac372691efd0a740fe0b4d7914a4 c++: non-standalone surrogate call template' -++ '[' 114 -gt 5 ']' -++ echo '... and 109 more' +++ echo '44ec516fcb54477932225f71261c39cad7f532af Merge v6.4.7 +4e382c2b468348d6208e5a18dbf1591a18170889 Linux 6.4.7 +8ab7147dfae7d70402540da584f8fe36591b1308 Revert "drm/amd/display: edp do not add non-edid timings" +e4f89142977e1108ed211f4e6ce30e8b21133d50 drm/amd/display: Add polling method to handle MST reply packet +cae69403a82cdb72c43e98ec9f857f5f0a1b3f7e drm/amd/display: Clean up errors & warnings in amdgpu_dm.c' +++ '[' 1329 -gt 5 ']' +++ echo '... and 1324 more' ++ for c in "${changed_components[@]}" +++ get_baseline_git glibc_rev +++ set -euf -o pipefail @@ -503,23 +488,27 @@ cde17323f950ac372691efd0a740fe0b4d7914a4 c++: non-standalone surrogate call temp +++ assert_with_msg 'ERROR: No glibc_rev in baseline git' '[' -f base-artifacts/git/glibc_rev ']' +++ set -euf -o pipefail +x +++ cat base-artifacts/git/glibc_rev -++ base_rev=721f30116ce653fffb0156e1298c8063833396e3 +++ base_rev=374cab0d95493c65bfcf8b7160a35d00258ff929 +++ get_current_git glibc_rev +++ set -euf -o pipefail +++ assert_with_msg 'ERROR: No glibc_rev in current git' '[' -f artifacts/git/glibc_rev ']' +++ set -euf -o pipefail +x +++ cat artifacts/git/glibc_rev -++ cur_rev=374cab0d95493c65bfcf8b7160a35d00258ff929 -+++ git -C glibc rev-list --count 721f30116ce653fffb0156e1298c8063833396e3..374cab0d95493c65bfcf8b7160a35d00258ff929 -++ c_commits=3 -++ new_commits=162 +++ cur_rev=14126ff059e98e9236633741fd323a1116299872 ++++ git -C glibc rev-list --count 374cab0d95493c65bfcf8b7160a35d00258ff929..14126ff059e98e9236633741fd323a1116299872 +++ c_commits=40 +++ new_commits=1482 ++ echo 'glibc commits:' -+++ git -C glibc log --pretty=oneline 721f30116ce653fffb0156e1298c8063833396e3..374cab0d95493c65bfcf8b7160a35d00258ff929 ++++ git -C glibc log --pretty=oneline 374cab0d95493c65bfcf8b7160a35d00258ff929..14126ff059e98e9236633741fd323a1116299872 +++ head -n5 -++ echo '374cab0d95493c65bfcf8b7160a35d00258ff929 Regenerate libc.pot -c6cb8783b5fb5896cb63fe9008b6a33351f3c777 configure: Use autoconf 2.71 -5a70ac9d39711528573439e01e249a8f825743ca Update sparc libm-test-ulps' -++ '[' 3 -gt 5 ']' ++++ true +++ echo '14126ff059e98e9236633741fd323a1116299872 install.texi: Update versions of most recent build tools +1d5355ddbb761ce653ff5916ff9b2d47ab54ee81 contrib.texi: Update for 2.38 +1547d6a64f4b981a06fd46ee446425a32558f2d0 <sys/platform/x86.h>: Add APX support +c8c8dbbf279b0ebaed3e871f626ba7dde876d247 translations: update cs, nl, vi +784ae968113011ce832b1808d4d42369f5d2e320 string: Fix tester build with fortify enable with gcc 6' +++ '[' 40 -gt 5 ']' +++ echo '... and 35 more' ++ bmk_print_result --short ++ false ++ local print_arg=--short @@ -570,7 +559,7 @@ c6cb8783b5fb5896cb63fe9008b6a33351f3c777 configure: Use autoconf 2.71 +++ bmk_data[tcwg_bmk-code_speed-cpu2017rate--llvm-arm]=tk1_32:spec2017_rate_nofortran +++ bmk_data[tcwg_bmk-code_speed-cpu2017speed--gnu-aarch64]=apm_64:spec2017_speed +++ bmk_data[tcwg_bmk-code_speed-cpu2017speed--llvm-aarch64]=apm_64:spec2017_speed -+++ bmk_data[tcwg_bmk-qc_speed-cpu2017rate--llvm-aarch64]=qc_64:spec2017_rate_nofortran ++++ bmk_data[tcwg_bmk-qc_speed-cpu2017rate--llvm-aarch64]=qc_64:spec2017_rate +++ bmk_data[tcwg_bmk-code_speed-spec2k6--gnu-aarch64]=tx1_64:spec2006_all +++ bmk_data[tcwg_bmk-code_speed-spec2k6--gnu-arm]=tk1_32:spec2006_all +++ bmk_data[tcwg_bmk-code_speed-spec2k6--llvm-aarch64]=tx1_64:spec2006_all @@ -688,33 +677,33 @@ c6cb8783b5fb5896cb63fe9008b6a33351f3c777 configure: Use autoconf 2.71 +++ assert_with_msg 'ERROR: No binutils_rev in baseline git' '[' -f base-artifacts/git/binutils_rev ']' +++ set -euf -o pipefail +x +++ cat base-artifacts/git/binutils_rev -++ base_rev=0d8de8f255d3333c7a122c419f7f75f3c6c45df5 +++ base_rev=fdc60e8cf680d6eb35813150b85283fc92ec2c0d +++ get_current_git binutils_rev +++ set -euf -o pipefail +++ assert_with_msg 'ERROR: No binutils_rev in current git' '[' -f artifacts/git/binutils_rev ']' +++ set -euf -o pipefail +x +++ cat artifacts/git/binutils_rev -++ cur_rev=fdc60e8cf680d6eb35813150b85283fc92ec2c0d -+++ git -C binutils rev-list --count 0d8de8f255d3333c7a122c419f7f75f3c6c45df5..fdc60e8cf680d6eb35813150b85283fc92ec2c0d -++ c_commits=45 -++ new_commits=45 +++ cur_rev=8e72ee1de8df0789c0ac593467d34387af388c83 ++++ git -C binutils rev-list --count fdc60e8cf680d6eb35813150b85283fc92ec2c0d..8e72ee1de8df0789c0ac593467d34387af388c83 +++ c_commits=113 +++ new_commits=113 ++ for c in "${changed_components[@]}" -+++ get_baseline_git gcc_rev ++++ get_baseline_git linux_rev +++ set -euf -o pipefail +++ local base_artifacts=base-artifacts -+++ assert_with_msg 'ERROR: No gcc_rev in baseline git' '[' -f base-artifacts/git/gcc_rev ']' ++++ assert_with_msg 'ERROR: No linux_rev in baseline git' '[' -f base-artifacts/git/linux_rev ']' +++ set -euf -o pipefail +x -+++ cat base-artifacts/git/gcc_rev -++ base_rev=8f1a26ee259f72e42405b9c5f2b161042ec4014b -+++ get_current_git gcc_rev ++++ cat base-artifacts/git/linux_rev +++ base_rev=3a9c734cf38026353ce3ea9ca342f4ee2cee4ddd ++++ get_current_git linux_rev +++ set -euf -o pipefail -+++ assert_with_msg 'ERROR: No gcc_rev in current git' '[' -f artifacts/git/gcc_rev ']' ++++ assert_with_msg 'ERROR: No linux_rev in current git' '[' -f artifacts/git/linux_rev ']' +++ set -euf -o pipefail +x -+++ cat artifacts/git/gcc_rev -++ cur_rev=c11a3aedec2649d72d1b7a3a2bd909c5863eefa1 -+++ git -C gcc rev-list --count 8f1a26ee259f72e42405b9c5f2b161042ec4014b..c11a3aedec2649d72d1b7a3a2bd909c5863eefa1 -++ c_commits=114 -++ new_commits=159 ++++ cat artifacts/git/linux_rev +++ cur_rev=44ec516fcb54477932225f71261c39cad7f532af ++++ git -C linux rev-list --count 3a9c734cf38026353ce3ea9ca342f4ee2cee4ddd..44ec516fcb54477932225f71261c39cad7f532af +++ c_commits=1329 +++ new_commits=1442 ++ for c in "${changed_components[@]}" +++ get_baseline_git glibc_rev +++ set -euf -o pipefail @@ -722,29 +711,27 @@ c6cb8783b5fb5896cb63fe9008b6a33351f3c777 configure: Use autoconf 2.71 +++ assert_with_msg 'ERROR: No glibc_rev in baseline git' '[' -f base-artifacts/git/glibc_rev ']' +++ set -euf -o pipefail +x +++ cat base-artifacts/git/glibc_rev -++ base_rev=721f30116ce653fffb0156e1298c8063833396e3 +++ base_rev=374cab0d95493c65bfcf8b7160a35d00258ff929 +++ get_current_git glibc_rev +++ set -euf -o pipefail +++ assert_with_msg 'ERROR: No glibc_rev in current git' '[' -f artifacts/git/glibc_rev ']' +++ set -euf -o pipefail +x +++ cat artifacts/git/glibc_rev -++ cur_rev=374cab0d95493c65bfcf8b7160a35d00258ff929 -+++ git -C glibc rev-list --count 721f30116ce653fffb0156e1298c8063833396e3..374cab0d95493c65bfcf8b7160a35d00258ff929 -++ c_commits=3 -++ new_commits=162 -+++ echo binutils gcc glibc +++ cur_rev=14126ff059e98e9236633741fd323a1116299872 ++++ git -C glibc rev-list --count 374cab0d95493c65bfcf8b7160a35d00258ff929..14126ff059e98e9236633741fd323a1116299872 +++ c_commits=40 +++ new_commits=1482 ++++ echo binutils linux glibc +++ tr ' ' , -++ components=binutils,gcc,glibc -++ echo '162 commits in binutils,gcc,glibc' +++ components=binutils,linux,glibc +++ echo '1482 commits in binutils,linux,glibc' # generate dashboard # generate_dashboard_squad ... Skipping # post_dashboard_squad ... Skipping => Not the first detection of this issue. Not sending mail. -# post_to_jira -Full stage ran successfully. -+ echo '[TCWG-CI] No change after commit: 162 commits in binutils,gcc,glibc' ++ echo '[TCWG-CI] No change after commit: 1482 commits in binutils,linux,glibc' + echo '# generate dashboard' + generate_dashboard_squad + local results_date @@ -759,9 +746,11 @@ Full stage ran successfully. + return + false + echo '=> Not the first detection of this issue. Not sending mail.' +# post_to_jira +Full stage ran successfully. + post_to_jira + echo '# post_to_jira' + false + false + echo 'Full stage ran successfully.' -16cc0ab785aa19adbe578ac41c0b54a151f03d175ec71a33cfb6cbdb06c971cd +91562ee3d0c485e014cdd4b1d77d0658c379ede22101a2b2bd201d42f4776376 diff --git a/notify/notify-init.log b/notify/notify-init.log index bcea4177..f628e620 100644 --- a/notify/notify-init.log +++ b/notify/notify-init.log @@ -1,6 +1,6 @@ -MOUNTS: /home/tcwg-buildslave/workspace/tcwg_bmk_1:/home/tcwg-buildslave/workspace/tcwg_bmk_1 /home/tcwg-buildslave/snapshots-ref:/home/tcwg-buildslave/snapshots-ref:ro /etc/ssh/ssh_host_dsa_key.pub:/etc/ssh/ssh_host_dsa_key.pub:ro /etc/ssh/ssh_host_rsa_key:/etc/ssh/ssh_host_rsa_key:ro /etc/ssh/ssh_host_ecdsa_key:/etc/ssh/ssh_host_ecdsa_key:ro /etc/ssh/ssh_host_ed25519_key.pub:/etc/ssh/ssh_host_ed25519_key.pub:ro /etc/ssh/ssh_host_dsa_key:/etc/ssh/ssh_host_dsa_key:ro /etc/ssh/ssh_host_ecdsa_key.pub:/etc/ssh/ssh_host_ecdsa_key.pub:ro /etc/ssh/ssh_host_ed25519_key:/etc/ssh/ssh_host_ed25519_key:ro /etc/ssh/ssh_host_rsa_key.pub:/etc/ssh/ssh_host_rsa_key.pub:ro ccache-tcwg_bmk-amd64-jammy:/home/tcwg-buildslave/.ccache /home/tcwg-benchmark -DEBUG: starting docker on build-10.tcwglab from build-10, date Tue Jul 18 06:52:13 PM UTC 2023 -ssh -Snone -oForwardAgent=no build-10.tcwglab docker-wrapper run --name 103-tcwg_bmk-code_speed-spec2k6--gnu-arm-master-O3-build -dtP -v /home/tcwg-buildslave/workspace/tcwg_bmk_1:/home/tcwg-buildslave/workspace/tcwg_bmk_1 -v /home/tcwg-buildslave/snapshots-ref:/home/tcwg-buildslave/snapshots-ref:ro -v /etc/ssh/ssh_host_dsa_key.pub:/etc/ssh/ssh_host_dsa_key.pub:ro -v /etc/ssh/ssh_host_rsa_key:/etc/ssh/ssh_host_rsa_key:ro -v /etc/ssh/ssh_host_ecdsa_key:/etc/ssh/ssh_host_ecdsa_key:ro -v /etc/ssh/ssh_host_ed25519_key.pub:/etc/ssh/ssh_host_ed25519_key.pub:ro -v /etc/ssh/ssh_host_dsa_key:/etc/ssh/ssh_host_dsa_key:ro -v /etc/ssh/ssh_host_ecdsa_key.pub:/etc/ssh/ssh_host_ecdsa_key.pub:ro -v /etc/ssh/ssh_host_ed25519_key:/etc/ssh/ssh_host_ed25519_key:ro -v /etc/ssh/ssh_host_rsa_key.pub:/etc/ssh/ssh_host_rsa_key.pub:ro -v ccache-tcwg_bmk-amd64-jammy:/home/tcwg-buildslave/.ccache -v /home/tcwg-benchmark --cap-add=SYS_PTRACE --security-opt seccomp:unconfined --memory=16000M --pids-limit=5000 --cpu-shares=1000 --cpuset-cpus 0-7 linaro/ci-amd64-tcwg-build-ubuntu:jammy +MOUNTS: /home/tcwg-buildslave/workspace/tcwg_bmk_1:/home/tcwg-buildslave/workspace/tcwg_bmk_1 /home/tcwg-buildslave/snapshots-ref:/home/tcwg-buildslave/snapshots-ref:ro /etc/ssh/ssh_host_ed25519_key:/etc/ssh/ssh_host_ed25519_key:ro /etc/ssh/ssh_host_dsa_key:/etc/ssh/ssh_host_dsa_key:ro /etc/ssh/ssh_host_ed25519_key.pub:/etc/ssh/ssh_host_ed25519_key.pub:ro /etc/ssh/ssh_host_rsa_key.pub:/etc/ssh/ssh_host_rsa_key.pub:ro /etc/ssh/ssh_host_dsa_key.pub:/etc/ssh/ssh_host_dsa_key.pub:ro /etc/ssh/ssh_host_ecdsa_key:/etc/ssh/ssh_host_ecdsa_key:ro /etc/ssh/ssh_host_ecdsa_key.pub:/etc/ssh/ssh_host_ecdsa_key.pub:ro /etc/ssh/ssh_host_rsa_key:/etc/ssh/ssh_host_rsa_key:ro ccache-tcwg_bmk-amd64-jammy:/home/tcwg-buildslave/.ccache /home/tcwg-benchmark +DEBUG: starting docker on dev-01.tcwglab from dev-01, date Sun Jul 30 09:13:41 AM UTC 2023 +ssh -Snone -oForwardAgent=no dev-01.tcwglab docker-wrapper run --name 108-tcwg_bmk-code_speed-spec2k6--gnu-arm-master-O3-build -dtP -v /home/tcwg-buildslave/workspace/tcwg_bmk_1:/home/tcwg-buildslave/workspace/tcwg_bmk_1 -v /home/tcwg-buildslave/snapshots-ref:/home/tcwg-buildslave/snapshots-ref:ro -v /etc/ssh/ssh_host_ed25519_key:/etc/ssh/ssh_host_ed25519_key:ro -v /etc/ssh/ssh_host_dsa_key:/etc/ssh/ssh_host_dsa_key:ro -v /etc/ssh/ssh_host_ed25519_key.pub:/etc/ssh/ssh_host_ed25519_key.pub:ro -v /etc/ssh/ssh_host_rsa_key.pub:/etc/ssh/ssh_host_rsa_key.pub:ro -v /etc/ssh/ssh_host_dsa_key.pub:/etc/ssh/ssh_host_dsa_key.pub:ro -v /etc/ssh/ssh_host_ecdsa_key:/etc/ssh/ssh_host_ecdsa_key:ro -v /etc/ssh/ssh_host_ecdsa_key.pub:/etc/ssh/ssh_host_ecdsa_key.pub:ro -v /etc/ssh/ssh_host_rsa_key:/etc/ssh/ssh_host_rsa_key:ro -v ccache-tcwg_bmk-amd64-jammy:/home/tcwg-buildslave/.ccache -v /home/tcwg-benchmark --memory=64000M --pids-limit=5000 --cpu-shares=1000 --cpuset-cpus 0-31 --cap-add=SYS_PTRACE --security-opt seccomp:unconfined linaro/ci-amd64-tcwg-build-ubuntu:jammy WARNING: Your kernel does not support swap limit capabilities or the cgroup is not mounted. Memory limited without swap. /home/tcwg-buildslave/workspace/tcwg_bmk_1/jenkins-scripts/round-robin-notify.sh @@rr[top_artifacts] artifacts --notify ignore __stage init __verbose true @@ -19,27 +19,27 @@ WARNING: Your kernel does not support swap limit capabilities or the cgroup is n ++ get_current_manifest '{rr[ci_project]}' ++ get_manifest artifacts/manifest.sh '{rr[ci_project]}' ++ set +x +# Debug traces : + ci_project=tcwg_bmk-code_speed-spec2k6 ++ get_current_manifest '{rr[ci_config]}' ++ get_manifest artifacts/manifest.sh '{rr[ci_config]}' -# Debug traces : ++ set +x + ci_config=gnu-arm-master-O3 + echo '# Debug traces :' ++ get_baseline_manifest BUILD_URL ++ get_manifest base-artifacts/manifest.sh BUILD_URL false ++ set +x -# Baseline : https://ci.linaro.org/job/tcwg_bmk-code_speed-spec2k6--gnu-arm-master-O3-build/102/ +# Baseline : https://ci.linaro.org/job/tcwg_bmk-code_speed-spec2k6--gnu-arm-master-O3-build/103/ # Using dir : base-artifacts -+ echo '# Baseline : https://ci.linaro.org/job/tcwg_bmk-code_speed-spec2k6--gnu-arm-master-O3-build/102/' ++ echo '# Baseline : https://ci.linaro.org/job/tcwg_bmk-code_speed-spec2k6--gnu-arm-master-O3-build/103/' + echo '# Using dir : base-artifacts' ++ get_current_manifest BUILD_URL ++ get_manifest artifacts/manifest.sh BUILD_URL ++ set +x -# Artifacts : https://ci.linaro.org/job/tcwg_bmk-code_speed-spec2k6--gnu-arm-master-O3-build/103/ +# Artifacts : https://ci.linaro.org/job/tcwg_bmk-code_speed-spec2k6--gnu-arm-master-O3-build/108/ # Using dir : artifacts -+ echo '# Artifacts : https://ci.linaro.org/job/tcwg_bmk-code_speed-spec2k6--gnu-arm-master-O3-build/103/' ++ echo '# Artifacts : https://ci.linaro.org/job/tcwg_bmk-code_speed-spec2k6--gnu-arm-master-O3-build/108/' + echo '# Using dir : artifacts' + echo '' + mkdir -p artifacts/notify @@ -61,19 +61,17 @@ WARNING: Your kernel does not support swap limit capabilities or the cgroup is n +++ set -euf -o pipefail +++ local c delim= +++ for c in ${rr[components]} -+++ '[' xgit://sourceware.org/git/binutils-gdb.git#fdc60e8cf680d6eb35813150b85283fc92ec2c0d '!=' xbaseline ']' ++++ '[' xgit://sourceware.org/git/binutils-gdb.git#8e72ee1de8df0789c0ac593467d34387af388c83 '!=' xbaseline ']' +++ echo -ne binutils +++ delim=' ' +++ for c in ${rr[components]} -+++ '[' xhttps://github.com/gcc-mirror/gcc.git#c11a3aedec2649d72d1b7a3a2bd909c5863eefa1 '!=' xbaseline ']' -+++ echo -ne ' gcc' -+++ delim=' ' ++++ '[' xbaseline '!=' xbaseline ']' +++ for c in ${rr[components]} -+++ '[' xhttps://git.linaro.org/kernel-org/linux-stable.git#3a9c734cf38026353ce3ea9ca342f4ee2cee4ddd '!=' xbaseline ']' ++++ '[' xhttps://git.linaro.org/kernel-org/linux-stable.git#44ec516fcb54477932225f71261c39cad7f532af '!=' xbaseline ']' +++ echo -ne ' linux' +++ delim=' ' +++ for c in ${rr[components]} -+++ '[' xgit://sourceware.org/git/glibc.git#374cab0d95493c65bfcf8b7160a35d00258ff929 '!=' xbaseline ']' ++++ '[' xgit://sourceware.org/git/glibc.git#14126ff059e98e9236633741fd323a1116299872 '!=' xbaseline ']' +++ echo -ne ' glibc' +++ delim=' ' +++ echo @@ -89,25 +87,10 @@ WARNING: Your kernel does not support swap limit capabilities or the cgroup is n +++ assert_with_msg 'ERROR: No binutils_rev in baseline git' '[' -f base-artifacts/git/binutils_rev ']' +++ set -euf -o pipefail +x +++ cat base-artifacts/git/binutils_rev -++ '[' xfdc60e8cf680d6eb35813150b85283fc92ec2c0d '!=' x0d8de8f255d3333c7a122c419f7f75f3c6c45df5 ']' +++ '[' x8e72ee1de8df0789c0ac593467d34387af388c83 '!=' xfdc60e8cf680d6eb35813150b85283fc92ec2c0d ']' ++ echo -ne binutils ++ delim=' ' ++ for c in $(print_updated_components) -+++ get_current_git gcc_rev -+++ set -euf -o pipefail -+++ assert_with_msg 'ERROR: No gcc_rev in current git' '[' -f artifacts/git/gcc_rev ']' -+++ set -euf -o pipefail +x -+++ cat artifacts/git/gcc_rev -+++ get_baseline_git gcc_rev -+++ set -euf -o pipefail -+++ local base_artifacts=base-artifacts -+++ assert_with_msg 'ERROR: No gcc_rev in baseline git' '[' -f base-artifacts/git/gcc_rev ']' -+++ set -euf -o pipefail +x -+++ cat base-artifacts/git/gcc_rev -++ '[' xc11a3aedec2649d72d1b7a3a2bd909c5863eefa1 '!=' x8f1a26ee259f72e42405b9c5f2b161042ec4014b ']' -++ echo -ne ' gcc' -++ delim=' ' -++ for c in $(print_updated_components) +++ get_current_git linux_rev +++ set -euf -o pipefail +++ assert_with_msg 'ERROR: No linux_rev in current git' '[' -f artifacts/git/linux_rev ']' @@ -119,7 +102,9 @@ WARNING: Your kernel does not support swap limit capabilities or the cgroup is n +++ assert_with_msg 'ERROR: No linux_rev in baseline git' '[' -f base-artifacts/git/linux_rev ']' +++ set -euf -o pipefail +x +++ cat base-artifacts/git/linux_rev -++ '[' x3a9c734cf38026353ce3ea9ca342f4ee2cee4ddd '!=' x3a9c734cf38026353ce3ea9ca342f4ee2cee4ddd ']' +++ '[' x44ec516fcb54477932225f71261c39cad7f532af '!=' x3a9c734cf38026353ce3ea9ca342f4ee2cee4ddd ']' +++ echo -ne ' linux' +++ delim=' ' ++ for c in $(print_updated_components) +++ get_current_git glibc_rev +++ set -euf -o pipefail @@ -132,19 +117,19 @@ WARNING: Your kernel does not support swap limit capabilities or the cgroup is n +++ assert_with_msg 'ERROR: No glibc_rev in baseline git' '[' -f base-artifacts/git/glibc_rev ']' +++ set -euf -o pipefail +x +++ cat base-artifacts/git/glibc_rev -++ '[' x374cab0d95493c65bfcf8b7160a35d00258ff929 '!=' x721f30116ce653fffb0156e1298c8063833396e3 ']' +++ '[' x14126ff059e98e9236633741fd323a1116299872 '!=' x374cab0d95493c65bfcf8b7160a35d00258ff929 ']' ++ echo -ne ' glibc' ++ delim=' ' ++ echo # Debug traces : -# change_kind=multiple_components : binutils gcc glibc +# change_kind=multiple_components : binutils linux glibc + local c base_rev cur_rev c_commits + '[' 3 = 0 ']' + '[' 3 = 1 ']' + change_kind=multiple_components + changed_single_component= + echo '# Debug traces :' -+ echo '# change_kind=multiple_components : binutils gcc glibc' ++ echo '# change_kind=multiple_components : binutils linux glibc' + for c in "${changed_components[@]}" ++ get_baseline_git binutils_rev ++ set -euf -o pipefail @@ -152,35 +137,35 @@ WARNING: Your kernel does not support swap limit capabilities or the cgroup is n ++ assert_with_msg 'ERROR: No binutils_rev in baseline git' '[' -f base-artifacts/git/binutils_rev ']' ++ set -euf -o pipefail +x ++ cat base-artifacts/git/binutils_rev -+ base_rev=0d8de8f255d3333c7a122c419f7f75f3c6c45df5 ++ base_rev=fdc60e8cf680d6eb35813150b85283fc92ec2c0d ++ get_current_git binutils_rev ++ set -euf -o pipefail ++ assert_with_msg 'ERROR: No binutils_rev in current git' '[' -f artifacts/git/binutils_rev ']' ++ set -euf -o pipefail +x ++ cat artifacts/git/binutils_rev -+ cur_rev=fdc60e8cf680d6eb35813150b85283fc92ec2c0d -++ git -C binutils rev-list --count 0d8de8f255d3333c7a122c419f7f75f3c6c45df5..fdc60e8cf680d6eb35813150b85283fc92ec2c0d -# rev for binutils : 0d8de8f255d3333c7a122c419f7f75f3c6c45df5..fdc60e8cf680d6eb35813150b85283fc92ec2c0d (45 commits) -+ c_commits=45 -+ echo '# rev for binutils : 0d8de8f255d3333c7a122c419f7f75f3c6c45df5..fdc60e8cf680d6eb35813150b85283fc92ec2c0d (45 commits)' ++ cur_rev=8e72ee1de8df0789c0ac593467d34387af388c83 +++ git -C binutils rev-list --count fdc60e8cf680d6eb35813150b85283fc92ec2c0d..8e72ee1de8df0789c0ac593467d34387af388c83 +# rev for binutils : fdc60e8cf680d6eb35813150b85283fc92ec2c0d..8e72ee1de8df0789c0ac593467d34387af388c83 (113 commits) ++ c_commits=113 ++ echo '# rev for binutils : fdc60e8cf680d6eb35813150b85283fc92ec2c0d..8e72ee1de8df0789c0ac593467d34387af388c83 (113 commits)' + for c in "${changed_components[@]}" -++ get_baseline_git gcc_rev +++ get_baseline_git linux_rev ++ set -euf -o pipefail ++ local base_artifacts=base-artifacts -++ assert_with_msg 'ERROR: No gcc_rev in baseline git' '[' -f base-artifacts/git/gcc_rev ']' +++ assert_with_msg 'ERROR: No linux_rev in baseline git' '[' -f base-artifacts/git/linux_rev ']' ++ set -euf -o pipefail +x -++ cat base-artifacts/git/gcc_rev -+ base_rev=8f1a26ee259f72e42405b9c5f2b161042ec4014b -++ get_current_git gcc_rev +++ cat base-artifacts/git/linux_rev ++ base_rev=3a9c734cf38026353ce3ea9ca342f4ee2cee4ddd +++ get_current_git linux_rev ++ set -euf -o pipefail -++ assert_with_msg 'ERROR: No gcc_rev in current git' '[' -f artifacts/git/gcc_rev ']' +++ assert_with_msg 'ERROR: No linux_rev in current git' '[' -f artifacts/git/linux_rev ']' ++ set -euf -o pipefail +x -++ cat artifacts/git/gcc_rev -+ cur_rev=c11a3aedec2649d72d1b7a3a2bd909c5863eefa1 -++ git -C gcc rev-list --count 8f1a26ee259f72e42405b9c5f2b161042ec4014b..c11a3aedec2649d72d1b7a3a2bd909c5863eefa1 -# rev for gcc : 8f1a26ee259f72e42405b9c5f2b161042ec4014b..c11a3aedec2649d72d1b7a3a2bd909c5863eefa1 (114 commits) -+ c_commits=114 -+ echo '# rev for gcc : 8f1a26ee259f72e42405b9c5f2b161042ec4014b..c11a3aedec2649d72d1b7a3a2bd909c5863eefa1 (114 commits)' +++ cat artifacts/git/linux_rev ++ cur_rev=44ec516fcb54477932225f71261c39cad7f532af +++ git -C linux rev-list --count 3a9c734cf38026353ce3ea9ca342f4ee2cee4ddd..44ec516fcb54477932225f71261c39cad7f532af +# rev for linux : 3a9c734cf38026353ce3ea9ca342f4ee2cee4ddd..44ec516fcb54477932225f71261c39cad7f532af (1329 commits) ++ c_commits=1329 ++ echo '# rev for linux : 3a9c734cf38026353ce3ea9ca342f4ee2cee4ddd..44ec516fcb54477932225f71261c39cad7f532af (1329 commits)' + for c in "${changed_components[@]}" ++ get_baseline_git glibc_rev ++ set -euf -o pipefail @@ -188,18 +173,18 @@ WARNING: Your kernel does not support swap limit capabilities or the cgroup is n ++ assert_with_msg 'ERROR: No glibc_rev in baseline git' '[' -f base-artifacts/git/glibc_rev ']' ++ set -euf -o pipefail +x ++ cat base-artifacts/git/glibc_rev -+ base_rev=721f30116ce653fffb0156e1298c8063833396e3 ++ base_rev=374cab0d95493c65bfcf8b7160a35d00258ff929 ++ get_current_git glibc_rev ++ set -euf -o pipefail ++ assert_with_msg 'ERROR: No glibc_rev in current git' '[' -f artifacts/git/glibc_rev ']' ++ set -euf -o pipefail +x ++ cat artifacts/git/glibc_rev -+ cur_rev=374cab0d95493c65bfcf8b7160a35d00258ff929 -++ git -C glibc rev-list --count 721f30116ce653fffb0156e1298c8063833396e3..374cab0d95493c65bfcf8b7160a35d00258ff929 -# rev for glibc : 721f30116ce653fffb0156e1298c8063833396e3..374cab0d95493c65bfcf8b7160a35d00258ff929 (3 commits) ++ cur_rev=14126ff059e98e9236633741fd323a1116299872 +++ git -C glibc rev-list --count 374cab0d95493c65bfcf8b7160a35d00258ff929..14126ff059e98e9236633741fd323a1116299872 +# rev for glibc : 374cab0d95493c65bfcf8b7160a35d00258ff929..14126ff059e98e9236633741fd323a1116299872 (40 commits) -+ c_commits=3 -+ echo '# rev for glibc : 721f30116ce653fffb0156e1298c8063833396e3..374cab0d95493c65bfcf8b7160a35d00258ff929 (3 commits)' ++ c_commits=40 ++ echo '# rev for glibc : 374cab0d95493c65bfcf8b7160a35d00258ff929..14126ff059e98e9236633741fd323a1116299872 (40 commits)' + echo '' + setup_stages_to_run + '[' xignore == xignore ']' @@ -217,18 +202,18 @@ WARNING: Your kernel does not support swap limit capabilities or the cgroup is n + print_result_f=bmk_print_result + print_config_f=bmk_print_config + generate_extra_details -+ set -euf -o pipefail # generate_extra_details -# post_interesting_commits -Init stage ran successfully. ++ set -euf -o pipefail + echo '# generate_extra_details' + post_interesting_commits init +# post_interesting_commits + set -euf -o pipefail + echo '# post_interesting_commits' + local stage=init + '[' multiple_components '!=' single_commit ']' + return +Init stage ran successfully. + '[' init '!=' full ']' + echo 'Init stage ran successfully.' + exit 0 -bdd79c991b3185db788430ffd88ebde39c7dac15b52a7b7923a57b0dc7bbdb04 +efffb0a0f0928ba760abe45bb0af9f44d83e1552a69ba8ac38966d609cc3251c diff --git a/notify/output-bmk-results.log b/notify/output-bmk-results.log index 20f2ab28..2011a391 100644 --- a/notify/output-bmk-results.log +++ b/notify/output-bmk-results.log @@ -104,34 +104,34 @@ output-bmk-results.py(23): self.csvwriter = csv.writer(self.outf) output-bmk-results.py(258): print(results_df) benchmark symbol ... status_x status_y 0 400.perlbench perlbench_base.default ... success success -3 401.bzip2 bzip2_base.default ... success success -9 403.gcc gcc_base.default ... success success -13 410.bwaves bwaves_base.default ... success success -16 416.gamess gamess_base.default ... success success -21 429.mcf mcf_base.default ... success success -24 433.milc milc_base.default ... success success -30 434.zeusmp zeusmp_base.default ... success success -33 435.gromacs gromacs_base.default ... success success -36 436.cactusADM cactusADM_base.default ... success success -38 437.leslie3d leslie3d_base.default ... success success -44 444.namd namd_base.default ... success success -51 445.gobmk gobmk_base.default ... success success -53 447.dealII dealII_base.default ... success success -59 450.soplex soplex_base.default ... success success -63 453.povray povray_base.default ... success success -68 454.calculix calculix_base.default ... success success -71 456.hmmer hmmer_base.default ... success success -73 458.sjeng sjeng_base.default ... success success -77 459.GemsFDTD GemsFDTD_base.default ... success success -83 462.libquantum libquantum_base.default ... success success -87 464.h264ref h264ref_base.default ... success success -92 465.tonto tonto_base.default ... success success -98 470.lbm lbm_base.default ... success success -100 471.omnetpp omnetpp_base.default ... success success -104 473.astar astar_base.default ... success success -108 481.wrf wrf_base.default ... success success -113 482.sphinx3 sphinx_livepretend_base.default ... success success -116 483.xalancbmk Xalan_base.default ... -1 -1 +2 401.bzip2 bzip2_base.default ... success success +8 403.gcc gcc_base.default ... success success +12 410.bwaves bwaves_base.default ... success success +15 416.gamess gamess_base.default ... success success +20 429.mcf mcf_base.default ... success success +23 433.milc milc_base.default ... success success +28 434.zeusmp zeusmp_base.default ... success success +31 435.gromacs gromacs_base.default ... success success +34 436.cactusADM cactusADM_base.default ... success success +36 437.leslie3d leslie3d_base.default ... success success +43 444.namd namd_base.default ... success success +50 445.gobmk gobmk_base.default ... success success +52 447.dealII dealII_base.default ... success success +58 450.soplex soplex_base.default ... success success +62 453.povray povray_base.default ... success success +67 454.calculix calculix_base.default ... success success +70 456.hmmer hmmer_base.default ... success success +72 458.sjeng sjeng_base.default ... success success +76 459.GemsFDTD GemsFDTD_base.default ... success success +82 462.libquantum libquantum_base.default ... success success +86 464.h264ref h264ref_base.default ... success success +91 465.tonto tonto_base.default ... success success +97 470.lbm lbm_base.default ... success success +99 471.omnetpp omnetpp_base.default ... success success +103 473.astar astar_base.default ... success success +107 481.wrf wrf_base.default ... success success +112 482.sphinx3 sphinx_livepretend_base.default ... success success +115 483.xalancbmk Xalan_base.default ... -1 -1 [29 rows x 20 columns] output-bmk-results.py(261): for index, row in results_df.iterrows(): @@ -559,34 +559,34 @@ output-bmk-results.py(23): self.csvwriter = csv.writer(self.outf) output-bmk-results.py(258): print(results_df) benchmark symbol ... status_x status_y 0 400.perlbench perlbench_base.default ... success success -3 401.bzip2 bzip2_base.default ... success success -9 403.gcc gcc_base.default ... success success -13 410.bwaves bwaves_base.default ... success success -16 416.gamess gamess_base.default ... success success -21 429.mcf mcf_base.default ... success success -24 433.milc milc_base.default ... success success -30 434.zeusmp zeusmp_base.default ... success success -33 435.gromacs gromacs_base.default ... success success -36 436.cactusADM cactusADM_base.default ... success success -38 437.leslie3d leslie3d_base.default ... success success -44 444.namd namd_base.default ... success success -51 445.gobmk gobmk_base.default ... success success -53 447.dealII dealII_base.default ... success success -59 450.soplex soplex_base.default ... success success -63 453.povray povray_base.default ... success success -68 454.calculix calculix_base.default ... success success -71 456.hmmer hmmer_base.default ... success success -73 458.sjeng sjeng_base.default ... success success -77 459.GemsFDTD GemsFDTD_base.default ... success success -83 462.libquantum libquantum_base.default ... success success -87 464.h264ref h264ref_base.default ... success success -92 465.tonto tonto_base.default ... success success -98 470.lbm lbm_base.default ... success success -100 471.omnetpp omnetpp_base.default ... success success -104 473.astar astar_base.default ... success success -108 481.wrf wrf_base.default ... success success -113 482.sphinx3 sphinx_livepretend_base.default ... success success -116 483.xalancbmk Xalan_base.default ... -1 -1 +2 401.bzip2 bzip2_base.default ... success success +8 403.gcc gcc_base.default ... success success +12 410.bwaves bwaves_base.default ... success success +15 416.gamess gamess_base.default ... success success +20 429.mcf mcf_base.default ... success success +23 433.milc milc_base.default ... success success +28 434.zeusmp zeusmp_base.default ... success success +31 435.gromacs gromacs_base.default ... success success +34 436.cactusADM cactusADM_base.default ... success success +36 437.leslie3d leslie3d_base.default ... success success +43 444.namd namd_base.default ... success success +50 445.gobmk gobmk_base.default ... success success +52 447.dealII dealII_base.default ... success success +58 450.soplex soplex_base.default ... success success +62 453.povray povray_base.default ... success success +67 454.calculix calculix_base.default ... success success +70 456.hmmer hmmer_base.default ... success success +72 458.sjeng sjeng_base.default ... success success +76 459.GemsFDTD GemsFDTD_base.default ... success success +82 462.libquantum libquantum_base.default ... success success +86 464.h264ref h264ref_base.default ... success success +91 465.tonto tonto_base.default ... success success +97 470.lbm lbm_base.default ... success success +99 471.omnetpp omnetpp_base.default ... success success +103 473.astar astar_base.default ... success success +107 481.wrf wrf_base.default ... success success +112 482.sphinx3 sphinx_livepretend_base.default ... success success +115 483.xalancbmk Xalan_base.default ... -1 -1 [29 rows x 20 columns] output-bmk-results.py(261): for index, row in results_df.iterrows(): @@ -1034,7 +1034,7 @@ output-bmk-results.py(105): return spec_thr 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=2% (threshold=3%) +DEBUG: checking exe.regression : 400.perlbench,perlbench_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(183): if metric in metric_utils.higher_regress_metrics: @@ -1061,7 +1061,7 @@ output-bmk-results.py(105): return spec_thr 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=-2% (threshold=3%) +DEBUG: checking exe.regression : 401.bzip2,bzip2_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(183): if metric in metric_utils.higher_regress_metrics: @@ -1304,7 +1304,7 @@ output-bmk-results.py(105): return spec_thr 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=1% (threshold=9.51%) +DEBUG: checking exe.regression : 437.leslie3d,leslie3d_base.default : sample=17% (threshold=9.540000000000001%) 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: @@ -1385,7 +1385,7 @@ output-bmk-results.py(105): return spec_thr 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=4% (threshold=3%) +DEBUG: checking exe.regression : 447.dealII,dealII_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(183): if metric in metric_utils.higher_regress_metrics: @@ -1412,7 +1412,7 @@ output-bmk-results.py(105): return spec_thr 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%) +DEBUG: checking exe.regression : 450.soplex,soplex_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(183): if metric in metric_utils.higher_regress_metrics: @@ -1439,7 +1439,7 @@ output-bmk-results.py(105): return spec_thr 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=1% (threshold=3%) +DEBUG: checking exe.regression : 453.povray,povray_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(183): if metric in metric_utils.higher_regress_metrics: @@ -1493,7 +1493,7 @@ output-bmk-results.py(105): return spec_thr 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=3.7800000000000002%) +DEBUG: checking exe.regression : 456.hmmer,hmmer_base.default : sample=-1% (threshold=3.7800000000000002%) 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: @@ -1547,7 +1547,7 @@ output-bmk-results.py(105): return spec_thr 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=1% (threshold=3%) +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(183): if metric in metric_utils.higher_regress_metrics: @@ -1601,7 +1601,7 @@ output-bmk-results.py(105): return spec_thr 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%) +DEBUG: checking exe.regression : 464.h264ref,h264ref_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(183): if metric in metric_utils.higher_regress_metrics: @@ -1628,7 +1628,7 @@ output-bmk-results.py(105): return spec_thr 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=-1% (threshold=3%) +DEBUG: checking exe.regression : 465.tonto,tonto_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(183): if metric in metric_utils.higher_regress_metrics: @@ -1736,7 +1736,7 @@ output-bmk-results.py(105): return spec_thr 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=-2% (threshold=3%) +DEBUG: checking exe.regression : 481.wrf,wrf_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(183): if metric in metric_utils.higher_regress_metrics: @@ -1763,7 +1763,7 @@ output-bmk-results.py(105): return spec_thr 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=1% (threshold=3%) +DEBUG: checking exe.regression : 482.sphinx3,sphinx_livepretend_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(183): if metric in metric_utils.higher_regress_metrics: @@ -1790,7 +1790,7 @@ output-bmk-results.py(105): return spec_thr 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 : 483.xalancbmk,Xalan_base.default : sample=3% (threshold=3%) +DEBUG: checking exe.regression : 483.xalancbmk,Xalan_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(183): if metric in metric_utils.higher_regress_metrics: @@ -1835,7 +1835,7 @@ output-bmk-results.py(105): return spec_thr 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=2% (threshold=3%) +DEBUG: checking exe.improvement : 400.perlbench,perlbench_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(192): if metric in metric_utils.higher_regress_metrics: @@ -1862,7 +1862,7 @@ output-bmk-results.py(105): return spec_thr 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=-2% (threshold=3%) +DEBUG: checking exe.improvement : 401.bzip2,bzip2_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(192): if metric in metric_utils.higher_regress_metrics: @@ -2105,12 +2105,37 @@ output-bmk-results.py(105): return spec_thr 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=1% (threshold=9.51%) +DEBUG: checking exe.improvement : 437.leslie3d,leslie3d_base.default : sample=17% (threshold=9.540000000000001%) 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(235): 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(137): bmk = row["benchmark"] +output-bmk-results.py(139): rel_value = row["rel_" + metric] +output-bmk-results.py(140): prev_value = row[metric + "_x"] +output-bmk-results.py(141): curr_value = row[metric + "_y"] +output-bmk-results.py(142): if metric == "num_vect_loops" or metric == "num_sve_loops": +output-bmk-results.py(152): suffix = "" +output-bmk-results.py(153): if metric == "sample": +output-bmk-results.py(154): prefix_regression = "slowed down by" +output-bmk-results.py(155): prefix_improvement = "sped up by" +output-bmk-results.py(156): suffix = "perf samples" +output-bmk-results.py(167): if sym_type=="symbol": +output-bmk-results.py(170): item=bmk +output-bmk-results.py(172): short_diag = "{1} {2}% - {0}".format(item, locals()["prefix_" + change_kind], abs(rel_value - 100)) +output-bmk-results.py(173): long_diag = "{0} - from {1} to {2} {3}".format(short_diag, prev_value, curr_value, suffix) +output-bmk-results.py(174): return abs(rel_value - 100), short_diag, long_diag +output-bmk-results.py(239): if metric == "sample" \ +output-bmk-results.py(240): and row['symbol_md5sum_x'] == row['symbol_md5sum_y'] \ +output-bmk-results.py(241): and row['symbol_md5sum_x'] != "-1" \ +output-bmk-results.py(242): and row['symbol_md5sum_x'] != "d41d8cd98f00b204e9800998ecf8427e": +output-bmk-results.py(243): 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(244): 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 @@ -2186,37 +2211,12 @@ output-bmk-results.py(105): return spec_thr 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=4% (threshold=3%) +DEBUG: checking exe.improvement : 447.dealII,dealII_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(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) -output-bmk-results.py(235): 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(137): bmk = row["benchmark"] -output-bmk-results.py(139): rel_value = row["rel_" + metric] -output-bmk-results.py(140): prev_value = row[metric + "_x"] -output-bmk-results.py(141): curr_value = row[metric + "_y"] -output-bmk-results.py(142): if metric == "num_vect_loops" or metric == "num_sve_loops": -output-bmk-results.py(152): suffix = "" -output-bmk-results.py(153): if metric == "sample": -output-bmk-results.py(154): prefix_regression = "slowed down by" -output-bmk-results.py(155): prefix_improvement = "sped up by" -output-bmk-results.py(156): suffix = "perf samples" -output-bmk-results.py(167): if sym_type=="symbol": -output-bmk-results.py(170): item=bmk -output-bmk-results.py(172): short_diag = "{1} {2}% - {0}".format(item, locals()["prefix_" + change_kind], abs(rel_value - 100)) -output-bmk-results.py(173): long_diag = "{0} - from {1} to {2} {3}".format(short_diag, prev_value, curr_value, suffix) -output-bmk-results.py(174): return abs(rel_value - 100), short_diag, long_diag -output-bmk-results.py(239): if metric == "sample" \ -output-bmk-results.py(240): and row['symbol_md5sum_x'] == row['symbol_md5sum_y'] \ -output-bmk-results.py(246): print("DEBUG: *** {0},{1} : {2}".format(row["benchmark"], row["symbol"], long_diag)) -DEBUG: *** 447.dealII,dealII_base.default : sped up by 4% - 447.dealII - from 5808 to 5578 perf samples -output-bmk-results.py(248): f_out.write_csv((percent_change, 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(249): if change_kind == "regression": +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 @@ -2238,7 +2238,7 @@ output-bmk-results.py(105): return spec_thr 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%) +DEBUG: checking exe.improvement : 450.soplex,soplex_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(192): if metric in metric_utils.higher_regress_metrics: @@ -2265,7 +2265,7 @@ output-bmk-results.py(105): return spec_thr 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=1% (threshold=3%) +DEBUG: checking exe.improvement : 453.povray,povray_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(192): if metric in metric_utils.higher_regress_metrics: @@ -2319,7 +2319,7 @@ output-bmk-results.py(105): return spec_thr 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=3.7800000000000002%) +DEBUG: checking exe.improvement : 456.hmmer,hmmer_base.default : sample=-1% (threshold=3.7800000000000002%) 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: @@ -2373,7 +2373,7 @@ output-bmk-results.py(105): return spec_thr 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=1% (threshold=3%) +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(192): if metric in metric_utils.higher_regress_metrics: @@ -2427,7 +2427,7 @@ output-bmk-results.py(105): return spec_thr 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%) +DEBUG: checking exe.improvement : 464.h264ref,h264ref_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(192): if metric in metric_utils.higher_regress_metrics: @@ -2454,7 +2454,7 @@ output-bmk-results.py(105): return spec_thr 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=-1% (threshold=3%) +DEBUG: checking exe.improvement : 465.tonto,tonto_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(192): if metric in metric_utils.higher_regress_metrics: @@ -2562,7 +2562,7 @@ output-bmk-results.py(105): return spec_thr 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=-2% (threshold=3%) +DEBUG: checking exe.improvement : 481.wrf,wrf_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(192): if metric in metric_utils.higher_regress_metrics: @@ -2589,7 +2589,7 @@ output-bmk-results.py(105): return spec_thr 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=1% (threshold=3%) +DEBUG: checking exe.improvement : 482.sphinx3,sphinx_livepretend_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(192): if metric in metric_utils.higher_regress_metrics: @@ -2616,7 +2616,7 @@ output-bmk-results.py(105): return spec_thr 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 : 483.xalancbmk,Xalan_base.default : sample=3% (threshold=3%) +DEBUG: checking exe.improvement : 483.xalancbmk,Xalan_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(192): if metric in metric_utils.higher_regress_metrics: @@ -2628,6 +2628,7 @@ output-bmk-results.py(253): f_out.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(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(218): f_out = Outfile("{0}/{1}.{2}".format(run_step_artifacts, sym_type, change_kind), "w", predicate=(details=="verbose")) @@ -2648,14 +2649,694 @@ output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mod 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(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(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(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) +output-bmk-results.py(105): return spec_thr +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=-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(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(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(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(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(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(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=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(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(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(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=-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(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(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(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=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(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(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(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=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(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(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(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=-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(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(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(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=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(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(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(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(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(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(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(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(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(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=-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(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(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(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=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(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(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(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=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(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(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(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=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(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(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(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=0% (threshold=17.07%) +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) + --- 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(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(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(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(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=-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(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(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(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=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(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(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(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=-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(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(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(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=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(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(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(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(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(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(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=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(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(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(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(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) + --- 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(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=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(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(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(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(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) + --- 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(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=-1% (threshold=15%) +DEBUG: checking symbol.regression : 436.cactusADM,[.] bench_staggeredleapfrog2_ : 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(183): if metric in metric_utils.higher_regress_metrics: @@ -2682,7 +3363,7 @@ output-bmk-results.py(105): return spec_thr 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=-3% (threshold=15%) +DEBUG: checking symbol.regression : 437.leslie3d,[.] fluxk_ : sample=22% (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: @@ -2709,7 +3390,7 @@ output-bmk-results.py(105): return spec_thr 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=-3% (threshold=15%) +DEBUG: checking symbol.regression : 437.leslie3d,[.] fluxj_ : sample=24% (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: @@ -2736,7 +3417,1087 @@ output-bmk-results.py(105): return spec_thr 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=5% (threshold=15%) +DEBUG: checking symbol.regression : 437.leslie3d,[.] fluxi_ : sample=23% (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) + --- 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(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=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(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(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(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=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(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(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(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,[.] extrapk_ : 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(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(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(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(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) + --- 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(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(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) + --- 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(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=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(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(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(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(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) + --- 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(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(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) + --- 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(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(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) + --- 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(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=-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(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(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(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=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(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(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(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=-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(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(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(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(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(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(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=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(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(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(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=-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(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(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(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=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(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(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(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(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(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(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=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(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(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(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=-13% (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) + --- 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(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=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(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(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(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=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(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(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(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=-6% (threshold=15.450000000000001%) +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) + --- 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(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(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(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(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=-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(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(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(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=-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(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(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(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=-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(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(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(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=-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(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(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(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=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(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(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(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(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(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(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=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(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(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(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(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(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(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(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) + --- 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(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(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(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(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=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(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(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(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=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(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(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(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=-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(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(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(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(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(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(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=-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(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(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(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=-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(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(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(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=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(183): if metric in metric_utils.higher_regress_metrics: @@ -2758,7 +4519,34 @@ output-bmk-results.py(110): return default_threshold[(change_kind,metric,mod 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=4% (threshold=15%) +DEBUG: checking symbol.regression : 465.tonto,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(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(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(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=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(183): if metric in metric_utils.higher_regress_metrics: @@ -2780,7 +4568,294 @@ output-bmk-results.py(110): return default_threshold[(change_kind,metric,mod 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=1% (threshold=15%) +DEBUG: checking symbol.regression : 465.tonto,[.] __sincosl : 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(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(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(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=-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(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 : 465.tonto,[.] __cexpl : 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(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(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(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=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(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(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(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,[.] _ZN12cMessageHeap7shiftupEi : 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(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 : 471.omnetpp,libc.so.6 : 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(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(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(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(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(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(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(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) + --- 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(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=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(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(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(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=-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(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(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(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=-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(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(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(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=-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(183): if metric in metric_utils.higher_regress_metrics: @@ -2829,7 +4904,88 @@ output-bmk-results.py(110): return default_threshold[(change_kind,metric,mod 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=0% (threshold=15%) +DEBUG: checking symbol.regression : 481.wrf,libc.so.6 : 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(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(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(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=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(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(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(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=-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(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(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(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 : 483.xalancbmk,[.] _ZN11xercesc_2_510ValueStore8containsEPKNS_13FieldValueMapE : 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(183): if metric in metric_utils.higher_regress_metrics: @@ -2851,7 +5007,61 @@ output-bmk-results.py(110): return default_threshold[(change_kind,metric,mod 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 : 483.xalancbmk,libc.so.6 : sample=-11% (threshold=15%) +DEBUG: checking symbol.regression : 483.xalancbmk,libc.so.6 : 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(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(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(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 : 483.xalancbmk,[.] _ZN10xalanc_1_819XalanDOMStringCache7releaseERNS_14XalanDOMStringE : 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) + --- 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(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 : 483.xalancbmk,[.] _ZN11xercesc_2_510ValueStore13isDuplicateOfEPNS_17DatatypeValidatorEPKtS2_S4_ : 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(183): if metric in metric_utils.higher_regress_metrics: @@ -2884,14 +5094,877 @@ output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mod 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(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(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(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) +output-bmk-results.py(105): return spec_thr +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=-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(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(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(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(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(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(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=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(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(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(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=-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(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(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(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=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(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(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(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=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(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(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(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=-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(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(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(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=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(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(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(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(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(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(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(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(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(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=-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(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(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(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=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(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(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(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=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(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(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(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=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(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(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(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=0% (threshold=17.07%) +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) + --- 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(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(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(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(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=-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(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(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(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=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(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(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(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=-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(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(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(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=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(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(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(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(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(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(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=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(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(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(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(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) + --- 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(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=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(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(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(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(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) + --- 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(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(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) + --- 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(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=22% (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(235): 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(137): bmk = row["benchmark"] +output-bmk-results.py(139): rel_value = row["rel_" + metric] +output-bmk-results.py(140): prev_value = row[metric + "_x"] +output-bmk-results.py(141): curr_value = row[metric + "_y"] +output-bmk-results.py(142): if metric == "num_vect_loops" or metric == "num_sve_loops": +output-bmk-results.py(152): suffix = "" +output-bmk-results.py(153): if metric == "sample": +output-bmk-results.py(154): prefix_regression = "slowed down by" +output-bmk-results.py(155): prefix_improvement = "sped up by" +output-bmk-results.py(156): suffix = "perf samples" +output-bmk-results.py(167): if sym_type=="symbol": +output-bmk-results.py(168): item=bmk+":"+row["symbol"] +output-bmk-results.py(172): short_diag = "{1} {2}% - {0}".format(item, locals()["prefix_" + change_kind], abs(rel_value - 100)) +output-bmk-results.py(173): long_diag = "{0} - from {1} to {2} {3}".format(short_diag, prev_value, curr_value, suffix) +output-bmk-results.py(174): return abs(rel_value - 100), short_diag, long_diag +output-bmk-results.py(239): if metric == "sample" \ +output-bmk-results.py(240): and row['symbol_md5sum_x'] == row['symbol_md5sum_y'] \ +output-bmk-results.py(241): and row['symbol_md5sum_x'] != "-1" \ +output-bmk-results.py(242): and row['symbol_md5sum_x'] != "d41d8cd98f00b204e9800998ecf8427e": +output-bmk-results.py(243): 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(244): 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(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(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=-1% (threshold=15%) +DEBUG: checking symbol.improvement : 437.leslie3d,[.] fluxj_ : sample=24% (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(235): 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(137): bmk = row["benchmark"] +output-bmk-results.py(139): rel_value = row["rel_" + metric] +output-bmk-results.py(140): prev_value = row[metric + "_x"] +output-bmk-results.py(141): curr_value = row[metric + "_y"] +output-bmk-results.py(142): if metric == "num_vect_loops" or metric == "num_sve_loops": +output-bmk-results.py(152): suffix = "" +output-bmk-results.py(153): if metric == "sample": +output-bmk-results.py(154): prefix_regression = "slowed down by" +output-bmk-results.py(155): prefix_improvement = "sped up by" +output-bmk-results.py(156): suffix = "perf samples" +output-bmk-results.py(167): if sym_type=="symbol": +output-bmk-results.py(168): item=bmk+":"+row["symbol"] +output-bmk-results.py(172): short_diag = "{1} {2}% - {0}".format(item, locals()["prefix_" + change_kind], abs(rel_value - 100)) +output-bmk-results.py(173): long_diag = "{0} - from {1} to {2} {3}".format(short_diag, prev_value, curr_value, suffix) +output-bmk-results.py(174): return abs(rel_value - 100), short_diag, long_diag +output-bmk-results.py(239): if metric == "sample" \ +output-bmk-results.py(240): and row['symbol_md5sum_x'] == row['symbol_md5sum_y'] \ +output-bmk-results.py(241): and row['symbol_md5sum_x'] != "-1" \ +output-bmk-results.py(242): and row['symbol_md5sum_x'] != "d41d8cd98f00b204e9800998ecf8427e": +output-bmk-results.py(243): 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(244): 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(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(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=23% (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(235): 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(137): bmk = row["benchmark"] +output-bmk-results.py(139): rel_value = row["rel_" + metric] +output-bmk-results.py(140): prev_value = row[metric + "_x"] +output-bmk-results.py(141): curr_value = row[metric + "_y"] +output-bmk-results.py(142): if metric == "num_vect_loops" or metric == "num_sve_loops": +output-bmk-results.py(152): suffix = "" +output-bmk-results.py(153): if metric == "sample": +output-bmk-results.py(154): prefix_regression = "slowed down by" +output-bmk-results.py(155): prefix_improvement = "sped up by" +output-bmk-results.py(156): suffix = "perf samples" +output-bmk-results.py(167): if sym_type=="symbol": +output-bmk-results.py(168): item=bmk+":"+row["symbol"] +output-bmk-results.py(172): short_diag = "{1} {2}% - {0}".format(item, locals()["prefix_" + change_kind], abs(rel_value - 100)) +output-bmk-results.py(173): long_diag = "{0} - from {1} to {2} {3}".format(short_diag, prev_value, curr_value, suffix) +output-bmk-results.py(174): return abs(rel_value - 100), short_diag, long_diag +output-bmk-results.py(239): if metric == "sample" \ +output-bmk-results.py(240): and row['symbol_md5sum_x'] == row['symbol_md5sum_y'] \ +output-bmk-results.py(241): and row['symbol_md5sum_x'] != "-1" \ +output-bmk-results.py(242): and row['symbol_md5sum_x'] != "d41d8cd98f00b204e9800998ecf8427e": +output-bmk-results.py(243): 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(244): 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(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(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=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(192): if metric in metric_utils.higher_regress_metrics: @@ -2918,7 +5991,7 @@ output-bmk-results.py(105): return spec_thr 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=-3% (threshold=15%) +DEBUG: checking symbol.improvement : 437.leslie3d,[.] extrapj_ : 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(192): if metric in metric_utils.higher_regress_metrics: @@ -2945,7 +6018,7 @@ output-bmk-results.py(105): return spec_thr 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=-3% (threshold=15%) +DEBUG: checking symbol.improvement : 437.leslie3d,[.] extrapk_ : 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(192): if metric in metric_utils.higher_regress_metrics: @@ -2972,7 +6045,979 @@ output-bmk-results.py(105): return spec_thr 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=5% (threshold=15%) +DEBUG: checking symbol.improvement : 444.namd,[.] _ZN20ComputeNonbondedUtil26calc_pair_energy_fullelectEP9nonbonded : 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(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(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(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(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) + --- 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(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=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(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(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(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(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) + --- 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(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(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) + --- 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(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(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) + --- 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(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=-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(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(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(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=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(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(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(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=-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(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(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(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(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(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(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=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(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(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(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=-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(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(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(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=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(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(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(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(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(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(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=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(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(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(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=-13% (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) + --- 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(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=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(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(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(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=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(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(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(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=-6% (threshold=15.450000000000001%) +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) + --- 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(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(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(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(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=-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(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(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(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=-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(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(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(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=-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(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(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(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=-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(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(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(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=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(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(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(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(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(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(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=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(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(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(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(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(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(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(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) + --- 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(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(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(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(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=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(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(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(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=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(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(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(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=-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(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(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(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(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(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(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=-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(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(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(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=-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(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(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(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=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(192): if metric in metric_utils.higher_regress_metrics: @@ -2994,7 +7039,159 @@ output-bmk-results.py(110): return default_threshold[(change_kind,metric,mod 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=4% (threshold=15%) +DEBUG: checking symbol.improvement : 465.tonto,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(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(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(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=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(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 : 465.tonto,[.] __sincosl : 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(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(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(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=-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(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 : 465.tonto,[.] __cexpl : 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(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(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(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=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(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(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(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,[.] _ZN12cMessageHeap7shiftupEi : 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(192): if metric in metric_utils.higher_regress_metrics: @@ -3016,7 +7213,169 @@ output-bmk-results.py(110): return default_threshold[(change_kind,metric,mod 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=1% (threshold=15%) +DEBUG: checking symbol.improvement : 471.omnetpp,libc.so.6 : 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(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(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(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(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(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(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(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) + --- 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(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=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(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(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(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=-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(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(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(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=-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(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(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(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=-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(192): if metric in metric_utils.higher_regress_metrics: @@ -3065,7 +7424,88 @@ output-bmk-results.py(110): return default_threshold[(change_kind,metric,mod 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=0% (threshold=15%) +DEBUG: checking symbol.improvement : 481.wrf,libc.so.6 : 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(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(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(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=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(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(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(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=-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(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(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(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 : 483.xalancbmk,[.] _ZN11xercesc_2_510ValueStore8containsEPKNS_13FieldValueMapE : 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(192): if metric in metric_utils.higher_regress_metrics: @@ -3087,7 +7527,61 @@ output-bmk-results.py(110): return default_threshold[(change_kind,metric,mod 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 : 483.xalancbmk,libc.so.6 : sample=-11% (threshold=15%) +DEBUG: checking symbol.improvement : 483.xalancbmk,libc.so.6 : 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(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(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(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 : 483.xalancbmk,[.] _ZN10xalanc_1_819XalanDOMStringCache7releaseERNS_14XalanDOMStringE : 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) + --- 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(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 : 483.xalancbmk,[.] _ZN11xercesc_2_510ValueStore13isDuplicateOfEPNS_17DatatypeValidatorEPKtS2_S4_ : 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(192): if metric in metric_utils.higher_regress_metrics: @@ -3109,7 +7603,6 @@ output-bmk-results.py(305): f_skip.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(306): f_regr.close() --- modulename: output-bmk-results, funcname: close output-bmk-results.py(29): if not self.outf: |