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authorTCWG BuildSlave <tcwg-buildslave@linaro.org>2023-11-22 22:58:08 +0000
committerTCWG BuildSlave <tcwg-buildslave@linaro.org>2023-11-23 00:50:12 +0000
commit6b69a7355c3a05c1ab54ada8a09f9858d48399b1 (patch)
treef5e69e830db61b9944b0ca8a1d10e8efbddb80bd /notify/output-bmk-results.log
parent6b798bc414bf3650a7225eb9b197001cf2a250bd (diff)
force: #93: 1: [TCWG CI] https://ci.linaro.org/job/tcwg_bmk-code_speed-cpu2017rate--llvm-arm-master-O2-build/93/
Results : | # reset_artifacts: | -10 | # build_bmk_llvm: | -3 | # benchmark -- -O2_marm: | 1 | # libc.so.6,slowed down by 50% - 544.nab_r:libc.so.6 - from 2 to 3 perf samples | # [.] __vfscanf_internal,slowed down by 100% - 544.nab_r:[.] __vfscanf_internal - from 1 to 2 perf samples check_regression status : 1
Diffstat (limited to 'notify/output-bmk-results.log')
-rw-r--r--notify/output-bmk-results.log937
1 files changed, 852 insertions, 85 deletions
diff --git a/notify/output-bmk-results.log b/notify/output-bmk-results.log
index 22e3bf4..64f7276 100644
--- a/notify/output-bmk-results.log
+++ b/notify/output-bmk-results.log
@@ -105,39 +105,24 @@ output-bmk-results.py(258): print(results_df)
benchmark symbol ... status_x status_y
0 500.perlbench_r perlbench_r_base.default ... failed-to-run failed-to-run
1 502.gcc_r cpugcc_r_base.default ... failed-to-run failed-to-run
-2 505.mcf_r mcf_r_base.default ... success -1
-6 508.namd_r namd_r_base.default ... failed-to-run failed-to-run
-7 510.parest_r parest_r_base.default ... failed-to-run failed-to-run
-8 511.povray_r povray_r_base.default ... failed-to-run failed-to-run
-9 519.lbm_r lbm_r_base.default ... failed-to-run failed-to-run
-11 519.lbm_r lbm_r_base.default ... failed-to-run failed-to-run
-12 520.omnetpp_r omnetpp_r_base.default ... failed-to-run failed-to-run
+2 505.mcf_r mcf_r_base.default ... -1 -1
+7 508.namd_r namd_r_base.default ... failed-to-run failed-to-run
+8 510.parest_r parest_r_base.default ... failed-to-run failed-to-run
+9 511.povray_r povray_r_base.default ... failed-to-run failed-to-run
+10 519.lbm_r lbm_r_base.default ... failed-to-run failed-to-run
+11 520.omnetpp_r omnetpp_r_base.default ... failed-to-run failed-to-run
+12 523.xalancbmk_r cpuxalan_r_base.default ... failed-to-run failed-to-run
13 523.xalancbmk_r cpuxalan_r_base.default ... failed-to-run failed-to-run
-16 523.xalancbmk_r cpuxalan_r_base.default ... failed-to-run failed-to-run
-17 525.x264_r x264_r_base.default ... failed-to-run failed-to-run
-18 526.blender_r blender_r_base.default ... failed-to-run failed-to-run
-19 531.deepsjeng_r deepsjeng_r_base.default ... success -1
-24 538.imagick_r imagick_r_base.default ... failed-to-run failed-to-run
-25 541.leela_r leela_r_base.default ... failed-to-run failed-to-run
-28 541.leela_r leela_r_base.default ... failed-to-run failed-to-run
-29 544.nab_r nab_r_base.default ... failed-to-run failed-to-run
-30 557.xz_r xz_r_base.default ... success -1
+16 525.x264_r x264_r_base.default ... failed-to-run failed-to-run
+17 526.blender_r blender_r_base.default ... failed-to-run failed-to-run
+18 531.deepsjeng_r deepsjeng_r_base.default ... -1 -1
+25 538.imagick_r imagick_r_base.default ... failed-to-run failed-to-run
+26 541.leela_r leela_r_base.default ... failed-to-run failed-to-run
+27 541.leela_r leela_r_base.default ... failed-to-run failed-to-run
+33 544.nab_r nab_r_base.default ... failed-to-run failed-to-run
+34 557.xz_r xz_r_base.default ... -1 -1
-[19 rows x 20 columns]
-output-bmk-results.py(261): for index, row in results_df.iterrows():
-output-bmk-results.py(263): classif, short_diag = get_status_diag(row)
- --- modulename: output-bmk-results, funcname: get_status_diag
-output-bmk-results.py(113): bmk = row["benchmark"]
-output-bmk-results.py(115): short_diag=""
-output-bmk-results.py(116): classif=""
-output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
-output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
-output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
-output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
-output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
-output-bmk-results.py(134): return classif, short_diag
-output-bmk-results.py(265): if classif != change_kind:
-output-bmk-results.py(266): continue;
+[18 rows x 20 columns]
output-bmk-results.py(261): for index, row in results_df.iterrows():
output-bmk-results.py(263): classif, short_diag = get_status_diag(row)
--- modulename: output-bmk-results, funcname: get_status_diag
@@ -410,39 +395,24 @@ output-bmk-results.py(258): print(results_df)
benchmark symbol ... status_x status_y
0 500.perlbench_r perlbench_r_base.default ... failed-to-run failed-to-run
1 502.gcc_r cpugcc_r_base.default ... failed-to-run failed-to-run
-2 505.mcf_r mcf_r_base.default ... success -1
-6 508.namd_r namd_r_base.default ... failed-to-run failed-to-run
-7 510.parest_r parest_r_base.default ... failed-to-run failed-to-run
-8 511.povray_r povray_r_base.default ... failed-to-run failed-to-run
-9 519.lbm_r lbm_r_base.default ... failed-to-run failed-to-run
-11 519.lbm_r lbm_r_base.default ... failed-to-run failed-to-run
-12 520.omnetpp_r omnetpp_r_base.default ... failed-to-run failed-to-run
+2 505.mcf_r mcf_r_base.default ... -1 -1
+7 508.namd_r namd_r_base.default ... failed-to-run failed-to-run
+8 510.parest_r parest_r_base.default ... failed-to-run failed-to-run
+9 511.povray_r povray_r_base.default ... failed-to-run failed-to-run
+10 519.lbm_r lbm_r_base.default ... failed-to-run failed-to-run
+11 520.omnetpp_r omnetpp_r_base.default ... failed-to-run failed-to-run
+12 523.xalancbmk_r cpuxalan_r_base.default ... failed-to-run failed-to-run
13 523.xalancbmk_r cpuxalan_r_base.default ... failed-to-run failed-to-run
-16 523.xalancbmk_r cpuxalan_r_base.default ... failed-to-run failed-to-run
-17 525.x264_r x264_r_base.default ... failed-to-run failed-to-run
-18 526.blender_r blender_r_base.default ... failed-to-run failed-to-run
-19 531.deepsjeng_r deepsjeng_r_base.default ... success -1
-24 538.imagick_r imagick_r_base.default ... failed-to-run failed-to-run
-25 541.leela_r leela_r_base.default ... failed-to-run failed-to-run
-28 541.leela_r leela_r_base.default ... failed-to-run failed-to-run
-29 544.nab_r nab_r_base.default ... failed-to-run failed-to-run
-30 557.xz_r xz_r_base.default ... success -1
+16 525.x264_r x264_r_base.default ... failed-to-run failed-to-run
+17 526.blender_r blender_r_base.default ... failed-to-run failed-to-run
+18 531.deepsjeng_r deepsjeng_r_base.default ... -1 -1
+25 538.imagick_r imagick_r_base.default ... failed-to-run failed-to-run
+26 541.leela_r leela_r_base.default ... failed-to-run failed-to-run
+27 541.leela_r leela_r_base.default ... failed-to-run failed-to-run
+33 544.nab_r nab_r_base.default ... failed-to-run failed-to-run
+34 557.xz_r xz_r_base.default ... -1 -1
-[19 rows x 20 columns]
-output-bmk-results.py(261): for index, row in results_df.iterrows():
-output-bmk-results.py(263): classif, short_diag = get_status_diag(row)
- --- modulename: output-bmk-results, funcname: get_status_diag
-output-bmk-results.py(113): bmk = row["benchmark"]
-output-bmk-results.py(115): short_diag=""
-output-bmk-results.py(116): classif=""
-output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build":
-output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run":
-output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run":
-output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success":
-output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success":
-output-bmk-results.py(134): return classif, short_diag
-output-bmk-results.py(265): if classif != change_kind:
-output-bmk-results.py(266): continue;
+[18 rows x 20 columns]
output-bmk-results.py(261): for index, row in results_df.iterrows():
output-bmk-results.py(263): classif, short_diag = get_status_diag(row)
--- modulename: output-bmk-results, funcname: get_status_diag
@@ -761,7 +731,142 @@ 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 : 531.deepsjeng_r,deepsjeng_r_base.default : sample=2% (threshold=3%)
+DEBUG: checking exe.regression : 519.lbm_r,lbm_r_base.default : sample=0% (threshold=201.14999999999998%)
+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 exe.regression : 523.xalancbmk_r,cpuxalan_r_base.default : sample=0% (threshold=300.0%)
+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 exe.regression : 523.xalancbmk_r,cpuxalan_r_base.default : sample=0% (threshold=300.0%)
+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 exe.regression : 531.deepsjeng_r,deepsjeng_r_base.default : sample=-3% (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:
+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 exe.regression : 541.leela_r,leela_r_base.default : sample=0% (threshold=300.0%)
+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 exe.regression : 541.leela_r,leela_r_base.default : sample=0% (threshold=300.0%)
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:
@@ -860,7 +965,142 @@ 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 : 531.deepsjeng_r,deepsjeng_r_base.default : sample=2% (threshold=3%)
+DEBUG: checking exe.improvement : 519.lbm_r,lbm_r_base.default : sample=0% (threshold=201.14999999999998%)
+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 exe.improvement : 523.xalancbmk_r,cpuxalan_r_base.default : sample=0% (threshold=300.0%)
+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 exe.improvement : 523.xalancbmk_r,cpuxalan_r_base.default : sample=0% (threshold=300.0%)
+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 exe.improvement : 531.deepsjeng_r,deepsjeng_r_base.default : sample=-3% (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(233): continue
+output-bmk-results.py(224): for index, row in out_df.iterrows():
+output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+ --- modulename: output-bmk-results, funcname: 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 exe.improvement : 541.leela_r,leela_r_base.default : sample=0% (threshold=300.0%)
+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 exe.improvement : 541.leela_r,leela_r_base.default : sample=0% (threshold=300.0%)
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:
@@ -959,7 +1199,7 @@ 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 : 505.mcf_r,[.] primal_bea_mpp : sample=-1% (threshold=15%)
+DEBUG: checking symbol.regression : 505.mcf_r,[.] primal_bea_mpp : 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:
@@ -986,7 +1226,77 @@ 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 : 505.mcf_r,[.] cost_compare : sample=1% (threshold=15%)
+DEBUG: checking symbol.regression : 505.mcf_r,[.] cost_compare : 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 : 508.namd_r,libc.so.6 : sample=0% (threshold=15%)
+output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+ --- modulename: output-bmk-results, funcname: is_entry_regression
+output-bmk-results.py(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(83): 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 : 523.xalancbmk_r,[.] _ZN11xercesc_2_710ValueStore8containsEPKNS_13FieldValueMapE : 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(83): 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 : 523.xalancbmk_r,[.] _ZN11xercesc_2_710ValueStore13isDuplicateOfEPNS_17DatatypeValidatorEPKtS2_S4_ : sample=-15% (threshold=15%)
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:
@@ -1013,7 +1323,7 @@ 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 : 531.deepsjeng_r,[.] _Z5fevalP7state_tiP12t_eval_comps : sample=4% (threshold=15%)
+DEBUG: checking symbol.regression : 531.deepsjeng_r,[.] _Z5fevalP7state_tiP12t_eval_comps : 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:
@@ -1040,7 +1350,7 @@ 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 : 531.deepsjeng_r,[.] _Z7ProbeTTP7state_tPiiiPjS1_S1_S1_S1_i : sample=2% (threshold=15%)
+DEBUG: checking symbol.regression : 531.deepsjeng_r,[.] _Z7ProbeTTP7state_tPiiiPjS1_S1_S1_S1_i : 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:
@@ -1067,7 +1377,7 @@ 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 : 531.deepsjeng_r,[.] _Z6searchP7state_tiiiii : sample=5% (threshold=16.32%)
+DEBUG: checking symbol.regression : 531.deepsjeng_r,[.] _Z6searchP7state_tiiiii : sample=-7% (threshold=16.59%)
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:
@@ -1094,7 +1404,124 @@ 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 : 531.deepsjeng_r,[.] _Z3seeP7state_tiiii : sample=1% (threshold=29.19%)
+DEBUG: checking symbol.regression : 531.deepsjeng_r,[.] _Z15FindFirstRemovePy : sample=-16% (threshold=15%)
+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(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(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 : 538.imagick_r,libc.so.6 : sample=0% (threshold=15%)
+output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+ --- modulename: output-bmk-results, funcname: is_entry_regression
+output-bmk-results.py(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 : 538.imagick_r,[.] _IO_fread : 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(83): 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 : 541.leela_r,[.] _ZN9FastBoard25get_pattern3_augment_specEiib : sample=50% (threshold=15%)
+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(83): 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 : 541.leela_r,[.] _ZN7MatcherC2Ev : 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:
@@ -1109,6 +1536,118 @@ 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(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 : 544.nab_r,libc.so.6 : sample=-50% (threshold=15%)
+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(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(246): print("DEBUG: *** {0},{1} : {2}".format(row["benchmark"], row["symbol"], long_diag))
+DEBUG: *** 544.nab_r,libc.so.6 : slowed down by 50% - 544.nab_r:libc.so.6 - from 2 to 3 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(250): f_regr.write("# {0},{1}\n".format(row["symbol"], long_diag))
+ --- modulename: output-bmk-results, funcname: write
+output-bmk-results.py(36): if not self.predicate or not self.outf:
+output-bmk-results.py(38): self.outf.write(string)
+output-bmk-results.py(251): f_ebp.write("++benchmarks {0} ".format(row["benchmark"]))
+ --- modulename: output-bmk-results, funcname: write
+output-bmk-results.py(36): if not self.predicate or not self.outf:
+output-bmk-results.py(38): self.outf.write(string)
+output-bmk-results.py(224): for index, row in out_df.iterrows():
+output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+ --- modulename: output-bmk-results, funcname: 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 : 544.nab_r,[.] __vfscanf_internal : sample=-100% (threshold=15%)
+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(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(246): print("DEBUG: *** {0},{1} : {2}".format(row["benchmark"], row["symbol"], long_diag))
+DEBUG: *** 544.nab_r,[.] __vfscanf_internal : slowed down by 100% - 544.nab_r:[.] __vfscanf_internal - from 1 to 2 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(250): f_regr.write("# {0},{1}\n".format(row["symbol"], long_diag))
+ --- modulename: output-bmk-results, funcname: write
+output-bmk-results.py(36): if not self.predicate or not self.outf:
+output-bmk-results.py(38): self.outf.write(string)
+output-bmk-results.py(251): f_ebp.write("++benchmarks {0} ".format(row["benchmark"]))
+ --- modulename: output-bmk-results, funcname: write
+output-bmk-results.py(36): if not self.predicate or not self.outf:
+output-bmk-results.py(38): self.outf.write(string)
+output-bmk-results.py(224): for index, row in out_df.iterrows():
+output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"])
+ --- modulename: output-bmk-results, funcname: 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'] )
@@ -1121,7 +1660,7 @@ 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 : 557.xz_r,[.] lzma_mf_bt4_find : sample=1% (threshold=15%)
+DEBUG: checking symbol.regression : 557.xz_r,[.] lzma_mf_bt4_find : 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:
@@ -1148,7 +1687,7 @@ 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 : 557.xz_r,[.] lzma_lzma_optimum_normal : sample=0% (threshold=15%)
+DEBUG: checking symbol.regression : 557.xz_r,[.] lzma_lzma_optimum_normal : 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:
@@ -1175,7 +1714,7 @@ 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 : 557.xz_r,[.] lzma_mf_bt4_skip : sample=-2% (threshold=15%)
+DEBUG: checking symbol.regression : 557.xz_r,[.] lzma_mf_bt4_skip : 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:
@@ -1187,7 +1726,6 @@ 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(301): output_bmk_results_1(sym_df, "symbol", "improvement", None, f_skip, None, run_step_artifacts, metric, mode, details)
--- modulename: output-bmk-results, funcname: output_bmk_results_1
output-bmk-results.py(218): f_out = Outfile("{0}/{1}.{2}".format(run_step_artifacts, sym_type, change_kind), "w", predicate=(details=="verbose"))
@@ -1247,7 +1785,7 @@ 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 : 505.mcf_r,[.] primal_bea_mpp : sample=-1% (threshold=15%)
+DEBUG: checking symbol.improvement : 505.mcf_r,[.] primal_bea_mpp : 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:
@@ -1274,7 +1812,77 @@ 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 : 505.mcf_r,[.] cost_compare : sample=1% (threshold=15%)
+DEBUG: checking symbol.improvement : 505.mcf_r,[.] cost_compare : 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 : 508.namd_r,libc.so.6 : sample=0% (threshold=15%)
+output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+ --- modulename: output-bmk-results, funcname: is_entry_improvement
+output-bmk-results.py(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(83): 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 : 523.xalancbmk_r,[.] _ZN11xercesc_2_710ValueStore8containsEPKNS_13FieldValueMapE : 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(83): 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 : 523.xalancbmk_r,[.] _ZN11xercesc_2_710ValueStore13isDuplicateOfEPNS_17DatatypeValidatorEPKtS2_S4_ : sample=-15% (threshold=15%)
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:
@@ -1301,7 +1909,7 @@ 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 : 531.deepsjeng_r,[.] _Z5fevalP7state_tiP12t_eval_comps : sample=4% (threshold=15%)
+DEBUG: checking symbol.improvement : 531.deepsjeng_r,[.] _Z5fevalP7state_tiP12t_eval_comps : 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:
@@ -1328,7 +1936,7 @@ 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 : 531.deepsjeng_r,[.] _Z7ProbeTTP7state_tPiiiPjS1_S1_S1_S1_i : sample=2% (threshold=15%)
+DEBUG: checking symbol.improvement : 531.deepsjeng_r,[.] _Z7ProbeTTP7state_tPiiiPjS1_S1_S1_S1_i : 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:
@@ -1355,7 +1963,7 @@ 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 : 531.deepsjeng_r,[.] _Z6searchP7state_tiiiii : sample=5% (threshold=16.32%)
+DEBUG: checking symbol.improvement : 531.deepsjeng_r,[.] _Z6searchP7state_tiiiii : sample=-7% (threshold=16.59%)
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:
@@ -1382,7 +1990,168 @@ 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 : 531.deepsjeng_r,[.] _Z3seeP7state_tiiii : sample=1% (threshold=29.19%)
+DEBUG: checking symbol.improvement : 531.deepsjeng_r,[.] _Z15FindFirstRemovePy : sample=-16% (threshold=15%)
+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 : 538.imagick_r,libc.so.6 : sample=0% (threshold=15%)
+output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold):
+ --- modulename: output-bmk-results, funcname: is_entry_improvement
+output-bmk-results.py(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 : 538.imagick_r,[.] _IO_fread : 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(83): 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 : 541.leela_r,[.] _ZN9FastBoard25get_pattern3_augment_specEiib : sample=50% (threshold=15%)
+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(83): 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 : 541.leela_r,[.] _ZN7MatcherC2Ev : 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(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 : 544.nab_r,libc.so.6 : sample=-50% (threshold=15%)
+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 : 544.nab_r,[.] __vfscanf_internal : sample=-100% (threshold=15%)
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:
@@ -1409,7 +2178,7 @@ 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 : 557.xz_r,[.] lzma_mf_bt4_find : sample=1% (threshold=15%)
+DEBUG: checking symbol.improvement : 557.xz_r,[.] lzma_mf_bt4_find : 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:
@@ -1436,7 +2205,7 @@ 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 : 557.xz_r,[.] lzma_lzma_optimum_normal : sample=0% (threshold=15%)
+DEBUG: checking symbol.improvement : 557.xz_r,[.] lzma_lzma_optimum_normal : 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:
@@ -1463,7 +2232,7 @@ 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 : 557.xz_r,[.] lzma_mf_bt4_skip : sample=-2% (threshold=15%)
+DEBUG: checking symbol.improvement : 557.xz_r,[.] lzma_mf_bt4_skip : 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:
@@ -1485,13 +2254,11 @@ 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:
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(307): f_ebp.close()
--- modulename: output-bmk-results, funcname: close
output-bmk-results.py(29): if not self.outf:
generated by cgit v1.2.3 (git 2.25.1) at 2024-07-03 22:07:32 +0000