diff options
Diffstat (limited to 'notify/output-bmk-results.log')
| -rw-r--r-- | notify/output-bmk-results.log | 562 |
1 files changed, 297 insertions, 265 deletions
diff --git a/notify/output-bmk-results.log b/notify/output-bmk-results.log index 64f7276..cf01817 100644 --- a/notify/output-bmk-results.log +++ b/notify/output-bmk-results.log @@ -106,21 +106,21 @@ output-bmk-results.py(258): print(results_df) 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 ... -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 -16 525.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 +8 508.namd_r namd_r_base.default ... failed-to-run failed-to-run +9 510.parest_r parest_r_base.default ... failed-to-run failed-to-run +10 511.povray_r povray_r_base.default ... failed-to-run failed-to-run +13 519.lbm_r lbm_r_base.default ... failed-to-run failed-to-run +14 520.omnetpp_r omnetpp_r_base.default ... failed-to-run failed-to-run +15 523.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 +19 525.x264_r x264_r_base.default ... failed-to-run failed-to-run +20 526.blender_r blender_r_base.default ... failed-to-run failed-to-run +21 531.deepsjeng_r deepsjeng_r_base.default ... -1 -1 +28 538.imagick_r imagick_r_base.default ... failed-to-run failed-to-run +29 541.leela_r leela_r_base.default ... failed-to-run failed-to-run +30 541.leela_r leela_r_base.default ... failed-to-run failed-to-run +35 544.nab_r nab_r_base.default ... failed-to-run failed-to-run +36 557.xz_r xz_r_base.default ... -1 -1 [18 rows x 20 columns] output-bmk-results.py(261): for index, row in results_df.iterrows(): @@ -396,21 +396,21 @@ output-bmk-results.py(258): print(results_df) 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 ... -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 -16 525.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 +8 508.namd_r namd_r_base.default ... failed-to-run failed-to-run +9 510.parest_r parest_r_base.default ... failed-to-run failed-to-run +10 511.povray_r povray_r_base.default ... failed-to-run failed-to-run +13 519.lbm_r lbm_r_base.default ... failed-to-run failed-to-run +14 520.omnetpp_r omnetpp_r_base.default ... failed-to-run failed-to-run +15 523.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 +19 525.x264_r x264_r_base.default ... failed-to-run failed-to-run +20 526.blender_r blender_r_base.default ... failed-to-run failed-to-run +21 531.deepsjeng_r deepsjeng_r_base.default ... -1 -1 +28 538.imagick_r imagick_r_base.default ... failed-to-run failed-to-run +29 541.leela_r leela_r_base.default ... failed-to-run failed-to-run +30 541.leela_r leela_r_base.default ... failed-to-run failed-to-run +35 544.nab_r nab_r_base.default ... failed-to-run failed-to-run +36 557.xz_r xz_r_base.default ... -1 -1 [18 rows x 20 columns] output-bmk-results.py(261): for index, row in results_df.iterrows(): @@ -721,44 +721,14 @@ output-bmk-results.py(60): var = specific_variability[ (specific_variability 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 : 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(83): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): -output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) -output-bmk-results.py(105): return 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 exe.regression : 523.xalancbmk_r,cpuxalan_r_base.default : sample=0% (threshold=300.0%) +DEBUG: checking exe.regression : 523.xalancbmk_r,cpuxalan_r_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: @@ -775,17 +745,14 @@ output-bmk-results.py(60): var = specific_variability[ (specific_variability 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(83): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): -output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) -output-bmk-results.py(105): return 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 exe.regression : 523.xalancbmk_r,cpuxalan_r_base.default : sample=0% (threshold=300.0%) +DEBUG: checking exe.regression : 523.xalancbmk_r,cpuxalan_r_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: @@ -812,7 +779,7 @@ 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 : 531.deepsjeng_r,deepsjeng_r_base.default : sample=-3% (threshold=3%) +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: @@ -955,44 +922,14 @@ output-bmk-results.py(60): var = specific_variability[ (specific_variability 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 : 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(83): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): -output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) -output-bmk-results.py(105): return 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 exe.improvement : 523.xalancbmk_r,cpuxalan_r_base.default : sample=0% (threshold=300.0%) +DEBUG: checking exe.improvement : 523.xalancbmk_r,cpuxalan_r_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: @@ -1009,17 +946,14 @@ output-bmk-results.py(60): var = specific_variability[ (specific_variability 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(83): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): -output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) -output-bmk-results.py(105): return 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 exe.improvement : 523.xalancbmk_r,cpuxalan_r_base.default : sample=0% (threshold=300.0%) +DEBUG: checking exe.improvement : 523.xalancbmk_r,cpuxalan_r_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: @@ -1046,7 +980,7 @@ 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 : 531.deepsjeng_r,deepsjeng_r_base.default : sample=-3% (threshold=3%) +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: @@ -1172,7 +1106,7 @@ 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,[.] price_out_impl : sample=-1% (threshold=15%) +DEBUG: checking symbol.regression : 505.mcf_r,[.] price_out_impl : 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: @@ -1199,7 +1133,7 @@ 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=3% (threshold=15%) +DEBUG: checking symbol.regression : 505.mcf_r,[.] primal_bea_mpp : 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: @@ -1226,7 +1160,7 @@ 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,[.] 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: @@ -1263,6 +1197,106 @@ 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 : 508.namd_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: *** 508.namd_r,[.] __vfscanf_internal : slowed down by 100% - 508.namd_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(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 : 519.lbm_r,[unknown] : 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 : 519.lbm_r,[k] 0xc001e188 : 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 @@ -1272,7 +1306,7 @@ 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 : 523.xalancbmk_r,[.] _ZN11xercesc_2_710ValueStore8containsEPKNS_13FieldValueMapE : sample=6% (threshold=15%) +DEBUG: checking symbol.regression : 523.xalancbmk_r,[.] _ZN11xercesc_2_710ValueStore8containsEPKNS_13FieldValueMapE : 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: @@ -1296,7 +1330,7 @@ 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 : 523.xalancbmk_r,[.] _ZN11xercesc_2_710ValueStore13isDuplicateOfEPNS_17DatatypeValidatorEPKtS2_S4_ : sample=-15% (threshold=15%) +DEBUG: checking symbol.regression : 523.xalancbmk_r,[.] _ZN11xercesc_2_710ValueStore13isDuplicateOfEPNS_17DatatypeValidatorEPKtS2_S4_ : 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: @@ -1323,7 +1357,7 @@ 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=0% (threshold=15%) +DEBUG: checking symbol.regression : 531.deepsjeng_r,[.] _Z5fevalP7state_tiP12t_eval_comps : 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: @@ -1350,7 +1384,7 @@ 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=4% (threshold=15%) +DEBUG: checking symbol.regression : 531.deepsjeng_r,[.] _Z7ProbeTTP7state_tPiiiPjS1_S1_S1_S1_i : 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: @@ -1377,7 +1411,7 @@ 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=-7% (threshold=16.59%) +DEBUG: checking symbol.regression : 531.deepsjeng_r,[.] _Z6searchP7state_tiiiii : sample=2% (threshold=17.04%) 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: @@ -1404,37 +1438,12 @@ 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 symbol.regression : 531.deepsjeng_r,[.] _Z15FindFirstRemovePy : sample=-16% (threshold=15%) +DEBUG: checking symbol.regression : 531.deepsjeng_r,[.] _Z3seeP7state_tiiii : sample=2% (threshold=30.21%) 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(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold @@ -1473,7 +1482,7 @@ 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 : 538.imagick_r,[.] _IO_fread : sample=0% (threshold=15%) +DEBUG: checking symbol.regression : 538.imagick_r,[.] _IO_fread : sample=33% (threshold=15%) 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: @@ -1497,12 +1506,37 @@ 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 : 541.leela_r,[.] _ZN9FastBoard25get_pattern3_augment_specEiib : sample=50% (threshold=15%) +DEBUG: checking symbol.regression : 541.leela_r,[.] _ZN7MatcherC2Ev : 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(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(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 @@ -1521,7 +1555,7 @@ 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 : 541.leela_r,[.] _ZN7MatcherC2Ev : sample=0% (threshold=15%) +DEBUG: checking symbol.regression : 541.leela_r,[.] _ZN9FastBoard25get_pattern3_augment_specEiib : 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: @@ -1543,46 +1577,12 @@ 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 : 544.nab_r,libc.so.6 : sample=-50% (threshold=15%) +DEBUG: checking symbol.regression : 544.nab_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(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(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold @@ -1599,46 +1599,12 @@ 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 : 544.nab_r,[.] __vfscanf_internal : sample=-100% (threshold=15%) +DEBUG: checking symbol.regression : 544.nab_r,[.] __vfscanf_internal : 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(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(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold @@ -1660,7 +1626,7 @@ 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=0% (threshold=15%) +DEBUG: checking symbol.regression : 557.xz_r,[.] lzma_mf_bt4_find : 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: @@ -1687,7 +1653,7 @@ 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=-3% (threshold=15%) +DEBUG: checking symbol.regression : 557.xz_r,[.] lzma_lzma_optimum_normal : 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: @@ -1714,7 +1680,7 @@ 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=3% (threshold=15%) +DEBUG: checking symbol.regression : 557.xz_r,[.] lzma_mf_bt4_skip : 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: @@ -1758,7 +1724,7 @@ 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,[.] price_out_impl : sample=-1% (threshold=15%) +DEBUG: checking symbol.improvement : 505.mcf_r,[.] price_out_impl : 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: @@ -1785,7 +1751,7 @@ 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=3% (threshold=15%) +DEBUG: checking symbol.improvement : 505.mcf_r,[.] primal_bea_mpp : 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: @@ -1812,7 +1778,7 @@ 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,[.] 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: @@ -1849,16 +1815,58 @@ 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(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(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,[.] __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: +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 : 523.xalancbmk_r,[.] _ZN11xercesc_2_710ValueStore8containsEPKNS_13FieldValueMapE : sample=6% (threshold=15%) +DEBUG: checking symbol.improvement : 519.lbm_r,[unknown] : 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 : 519.lbm_r,[k] 0xc001e188 : 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: @@ -1882,7 +1890,7 @@ 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 : 523.xalancbmk_r,[.] _ZN11xercesc_2_710ValueStore13isDuplicateOfEPNS_17DatatypeValidatorEPKtS2_S4_ : sample=-15% (threshold=15%) +DEBUG: checking symbol.improvement : 523.xalancbmk_r,[.] _ZN11xercesc_2_710ValueStore8containsEPKNS_13FieldValueMapE : 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: @@ -1899,17 +1907,14 @@ output-bmk-results.py(60): var = specific_variability[ (specific_variability 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(83): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): -output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) -output-bmk-results.py(105): return 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 : 531.deepsjeng_r,[.] _Z5fevalP7state_tiP12t_eval_comps : sample=0% (threshold=15%) +DEBUG: checking symbol.improvement : 523.xalancbmk_r,[.] _ZN11xercesc_2_710ValueStore13isDuplicateOfEPNS_17DatatypeValidatorEPKtS2_S4_ : 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: @@ -1936,7 +1941,7 @@ 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=4% (threshold=15%) +DEBUG: checking symbol.improvement : 531.deepsjeng_r,[.] _Z5fevalP7state_tiP12t_eval_comps : 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: @@ -1963,7 +1968,7 @@ 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=-7% (threshold=16.59%) +DEBUG: checking symbol.improvement : 531.deepsjeng_r,[.] _Z7ProbeTTP7state_tPiiiPjS1_S1_S1_S1_i : 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: @@ -1990,7 +1995,7 @@ 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,[.] _Z15FindFirstRemovePy : sample=-16% (threshold=15%) +DEBUG: checking symbol.improvement : 531.deepsjeng_r,[.] _Z6searchP7state_tiiiii : sample=2% (threshold=17.04%) 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: @@ -2005,14 +2010,19 @@ 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 : 538.imagick_r,libc.so.6 : sample=0% (threshold=15%) +DEBUG: checking symbol.improvement : 531.deepsjeng_r,[.] _Z3seeP7state_tiiii : sample=2% (threshold=30.21%) 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: @@ -2034,7 +2044,7 @@ 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 : 538.imagick_r,[.] _IO_fread : sample=0% (threshold=15%) +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: @@ -2049,16 +2059,14 @@ 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(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(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 : 541.leela_r,[.] _ZN9FastBoard25get_pattern3_augment_specEiib : sample=50% (threshold=15%) +DEBUG: checking symbol.improvement : 538.imagick_r,[.] _IO_fread : sample=33% (threshold=15%) 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: @@ -2083,12 +2091,13 @@ output-bmk-results.py(174): return abs(rel_value - 100), short_diag, long_di 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)) +output-bmk-results.py(246): print("DEBUG: *** {0},{1} : {2}".format(row["benchmark"], row["symbol"], long_diag)) +DEBUG: *** 538.imagick_r,[.] _IO_fread : sped up by 33% - 538.imagick_r:[.] _IO_fread - from 3 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(244): continue +output-bmk-results.py(249): if change_kind == "regression": output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold @@ -2107,7 +2116,31 @@ 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 : 541.leela_r,[.] _ZN7MatcherC2Ev : sample=0% (threshold=15%) +DEBUG: checking symbol.improvement : 541.leela_r,[.] _ZN7MatcherC2Ev : 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: +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=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: @@ -2129,7 +2162,7 @@ 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 : 544.nab_r,libc.so.6 : sample=-50% (threshold=15%) +DEBUG: checking symbol.improvement : 544.nab_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: @@ -2151,7 +2184,7 @@ 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 : 544.nab_r,[.] __vfscanf_internal : sample=-100% (threshold=15%) +DEBUG: checking symbol.improvement : 544.nab_r,[.] __vfscanf_internal : 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: @@ -2178,7 +2211,7 @@ 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=0% (threshold=15%) +DEBUG: checking symbol.improvement : 557.xz_r,[.] lzma_mf_bt4_find : 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: @@ -2205,7 +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 symbol.improvement : 557.xz_r,[.] lzma_lzma_optimum_normal : sample=-3% (threshold=15%) +DEBUG: checking symbol.improvement : 557.xz_r,[.] lzma_lzma_optimum_normal : 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: @@ -2232,7 +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 symbol.improvement : 557.xz_r,[.] lzma_mf_bt4_skip : sample=3% (threshold=15%) +DEBUG: checking symbol.improvement : 557.xz_r,[.] lzma_mf_bt4_skip : 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: @@ -2244,7 +2277,6 @@ 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(303): f_ebp.write("\n") --- modulename: output-bmk-results, funcname: write output-bmk-results.py(36): if not self.predicate or not self.outf: |