diff options
Diffstat (limited to 'notify/output-bmk-results.log')
| -rw-r--r-- | notify/output-bmk-results.log | 3042 |
1 files changed, 1425 insertions, 1617 deletions
diff --git a/notify/output-bmk-results.log b/notify/output-bmk-results.log index 284b449..0639cd0 100644 --- a/notify/output-bmk-results.log +++ b/notify/output-bmk-results.log @@ -1,106 +1,106 @@ --- modulename: output-bmk-results, funcname: <module> <string>(1): --- modulename: output-bmk-results, funcname: main -output-bmk-results.py(278): results_csv = sys.argv[1] -output-bmk-results.py(279): variability_file = sys.argv[2] -output-bmk-results.py(280): run_step_artifacts_dir = sys.argv[3] -output-bmk-results.py(281): metric = sys.argv[4] -output-bmk-results.py(282): mode = sys.argv[5] -output-bmk-results.py(283): details = sys.argv[6] -output-bmk-results.py(285): merged_df = read_results_csv(results_csv) +output-bmk-results.py(287): results_csv = sys.argv[1] +output-bmk-results.py(288): variability_file = sys.argv[2] +output-bmk-results.py(289): run_step_artifacts_dir = sys.argv[3] +output-bmk-results.py(290): metric = sys.argv[4] +output-bmk-results.py(291): mode = sys.argv[5] +output-bmk-results.py(292): details = sys.argv[6] +output-bmk-results.py(294): merged_df = read_results_csv(results_csv) --- modulename: output-bmk-results, funcname: read_results_csv -output-bmk-results.py(268): df = pd.read_csv(results_csv) -output-bmk-results.py(269): df = df.fillna(-1) -output-bmk-results.py(271): for metric in get_comparable_metrics(df): +output-bmk-results.py(277): df = pd.read_csv(results_csv) +output-bmk-results.py(278): df = df.fillna(-1) +output-bmk-results.py(280): for metric in get_comparable_metrics(df): --- modulename: output-bmk-results, funcname: get_comparable_metrics -output-bmk-results.py(189): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ +output-bmk-results.py(198): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ --- modulename: output-bmk-results, funcname: <genexpr> -output-bmk-results.py(189): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ +output-bmk-results.py(198): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ --- modulename: output-bmk-results, funcname: <genexpr> -output-bmk-results.py(189): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ +output-bmk-results.py(198): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ --- modulename: output-bmk-results, funcname: <genexpr> -output-bmk-results.py(189): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ +output-bmk-results.py(198): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ --- modulename: output-bmk-results, funcname: <genexpr> -output-bmk-results.py(189): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ +output-bmk-results.py(198): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ --- modulename: output-bmk-results, funcname: <genexpr> -output-bmk-results.py(189): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ +output-bmk-results.py(198): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ --- modulename: output-bmk-results, funcname: <genexpr> -output-bmk-results.py(189): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ -output-bmk-results.py(189): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ -output-bmk-results.py(189): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ -output-bmk-results.py(189): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ -output-bmk-results.py(189): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ -output-bmk-results.py(189): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ -output-bmk-results.py(189): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ -output-bmk-results.py(189): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ -output-bmk-results.py(189): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ -output-bmk-results.py(189): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ -output-bmk-results.py(189): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ -output-bmk-results.py(190): & metric_utils.comparable_metrics -output-bmk-results.py(189): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ -output-bmk-results.py(272): df["rel_" + metric] = df["rel_" + metric].astype("int") -output-bmk-results.py(273): df[metric + "_x"] = df[metric + "_x"].astype("int") -output-bmk-results.py(274): df[metric + "_y"] = df[metric + "_y"].astype("int") -output-bmk-results.py(271): for metric in get_comparable_metrics(df): -output-bmk-results.py(272): df["rel_" + metric] = df["rel_" + metric].astype("int") -output-bmk-results.py(273): df[metric + "_x"] = df[metric + "_x"].astype("int") -output-bmk-results.py(274): df[metric + "_y"] = df[metric + "_y"].astype("int") -output-bmk-results.py(271): for metric in get_comparable_metrics(df): -output-bmk-results.py(272): df["rel_" + metric] = df["rel_" + metric].astype("int") -output-bmk-results.py(273): df[metric + "_x"] = df[metric + "_x"].astype("int") -output-bmk-results.py(274): df[metric + "_y"] = df[metric + "_y"].astype("int") -output-bmk-results.py(271): for metric in get_comparable_metrics(df): -output-bmk-results.py(272): df["rel_" + metric] = df["rel_" + metric].astype("int") -output-bmk-results.py(273): df[metric + "_x"] = df[metric + "_x"].astype("int") -output-bmk-results.py(274): df[metric + "_y"] = df[metric + "_y"].astype("int") -output-bmk-results.py(271): for metric in get_comparable_metrics(df): -output-bmk-results.py(275): return df -output-bmk-results.py(286): read_specific_variability_file(variability_file) +output-bmk-results.py(198): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ +output-bmk-results.py(198): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ +output-bmk-results.py(198): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ +output-bmk-results.py(198): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ +output-bmk-results.py(198): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ +output-bmk-results.py(198): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ +output-bmk-results.py(198): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ +output-bmk-results.py(198): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ +output-bmk-results.py(198): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ +output-bmk-results.py(198): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ +output-bmk-results.py(198): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ +output-bmk-results.py(199): & metric_utils.comparable_metrics +output-bmk-results.py(198): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ +output-bmk-results.py(281): df["rel_" + metric] = df["rel_" + metric].astype("int") +output-bmk-results.py(282): df[metric + "_x"] = df[metric + "_x"].astype("int") +output-bmk-results.py(283): df[metric + "_y"] = df[metric + "_y"].astype("int") +output-bmk-results.py(280): for metric in get_comparable_metrics(df): +output-bmk-results.py(281): df["rel_" + metric] = df["rel_" + metric].astype("int") +output-bmk-results.py(282): df[metric + "_x"] = df[metric + "_x"].astype("int") +output-bmk-results.py(283): df[metric + "_y"] = df[metric + "_y"].astype("int") +output-bmk-results.py(280): for metric in get_comparable_metrics(df): +output-bmk-results.py(281): df["rel_" + metric] = df["rel_" + metric].astype("int") +output-bmk-results.py(282): df[metric + "_x"] = df[metric + "_x"].astype("int") +output-bmk-results.py(283): df[metric + "_y"] = df[metric + "_y"].astype("int") +output-bmk-results.py(280): for metric in get_comparable_metrics(df): +output-bmk-results.py(281): df["rel_" + metric] = df["rel_" + metric].astype("int") +output-bmk-results.py(282): df[metric + "_x"] = df[metric + "_x"].astype("int") +output-bmk-results.py(283): df[metric + "_y"] = df[metric + "_y"].astype("int") +output-bmk-results.py(280): for metric in get_comparable_metrics(df): +output-bmk-results.py(284): return df +output-bmk-results.py(295): read_specific_variability_file(variability_file) --- modulename: output-bmk-results, funcname: read_specific_variability_file output-bmk-results.py(51): if not os.path.exists(bmk_specific_filename): output-bmk-results.py(53): specific_variability = pd.read_csv(bmk_specific_filename, index_col=False) -output-bmk-results.py(287): output_bmk_results(merged_df, run_step_artifacts_dir, metric, mode, details) +output-bmk-results.py(296): output_bmk_results(merged_df, run_step_artifacts_dir, metric, mode, details) --- modulename: output-bmk-results, funcname: output_bmk_results -output-bmk-results.py(239): f_regr = Outfile("{0}/results.regressions".format(run_step_artifacts), "w") +output-bmk-results.py(248): f_regr = Outfile("{0}/results.regressions".format(run_step_artifacts), "w") --- modulename: output-bmk-results, funcname: __init__ output-bmk-results.py(19): self.filename=filename output-bmk-results.py(20): self.predicate=predicate output-bmk-results.py(21): if predicate: output-bmk-results.py(22): self.outf = open(filename, mode) output-bmk-results.py(23): self.csvwriter = csv.writer(self.outf) -output-bmk-results.py(240): f_ebp = Outfile("{0}/extra-bisect-params".format(run_step_artifacts), "w") +output-bmk-results.py(249): f_ebp = Outfile("{0}/extra-bisect-params".format(run_step_artifacts), "w") --- modulename: output-bmk-results, funcname: __init__ output-bmk-results.py(19): self.filename=filename output-bmk-results.py(20): self.predicate=predicate output-bmk-results.py(21): if predicate: output-bmk-results.py(22): self.outf = open(filename, mode) output-bmk-results.py(23): self.csvwriter = csv.writer(self.outf) -output-bmk-results.py(241): f_skip = Outfile("{0}/any.skipped".format(run_step_artifacts), "w", predicate=(details=="verbose")) +output-bmk-results.py(250): f_skip = Outfile("{0}/any.skipped".format(run_step_artifacts), "w", predicate=(details=="verbose")) --- modulename: output-bmk-results, funcname: __init__ output-bmk-results.py(19): self.filename=filename output-bmk-results.py(20): self.predicate=predicate output-bmk-results.py(21): if predicate: output-bmk-results.py(22): self.outf = open(filename, mode) output-bmk-results.py(23): self.csvwriter = csv.writer(self.outf) -output-bmk-results.py(243): f_ebp.write("extra_build_params=") +output-bmk-results.py(252): f_ebp.write("extra_build_params=") --- modulename: output-bmk-results, funcname: write output-bmk-results.py(36): if not self.predicate or not self.outf: output-bmk-results.py(38): self.outf.write(string) -output-bmk-results.py(247): df = merged_df[merged_df["benchmark"] != "Mean"] -output-bmk-results.py(250): exe_df = df[df["symbol"].str.endswith("_base.default")] -output-bmk-results.py(251): sym_df = df[~df["symbol"].str.endswith("_base.default")] -output-bmk-results.py(253): output_bmk_results_1(exe_df, "exe", "regression", f_regr, f_skip, f_ebp, run_step_artifacts, metric, mode, details) +output-bmk-results.py(256): df = merged_df[merged_df["benchmark"] != "Mean"] +output-bmk-results.py(259): exe_df = df[df["symbol"].str.endswith("_base.default")] +output-bmk-results.py(260): sym_df = df[~df["symbol"].str.endswith("_base.default")] +output-bmk-results.py(262): output_bmk_results_1(exe_df, "exe", "regression", f_regr, f_skip, f_ebp, run_step_artifacts, metric, mode, details) --- modulename: output-bmk-results, funcname: output_bmk_results_1 -output-bmk-results.py(201): f_out = Outfile("{0}/{1}.{2}".format(run_step_artifacts, sym_type, change_kind), "w", predicate=(details=="verbose")) +output-bmk-results.py(210): f_out = Outfile("{0}/{1}.{2}".format(run_step_artifacts, sym_type, change_kind), "w", predicate=(details=="verbose")) --- modulename: output-bmk-results, funcname: __init__ output-bmk-results.py(19): self.filename=filename output-bmk-results.py(20): self.predicate=predicate output-bmk-results.py(21): if predicate: output-bmk-results.py(22): self.outf = open(filename, mode) output-bmk-results.py(23): self.csvwriter = csv.writer(self.outf) -output-bmk-results.py(203): rel_metric = "rel_" + metric -output-bmk-results.py(204): out_df = results_df[results_df[rel_metric] != -1] -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(212): rel_metric = "rel_" + metric +output-bmk-results.py(213): out_df = results_df[results_df[rel_metric] != -1] +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -117,17 +117,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking exe.regression : 500.perlbench_r,perlbench_r_base.default : sample=1% (threshold=3%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking exe.regression : 500.perlbench_r,perlbench_r_base.default : sample=-1% (threshold=3%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -144,17 +144,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking exe.regression : 502.gcc_r,cpugcc_r_base.default : sample=-3% (threshold=3%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking exe.regression : 502.gcc_r,cpugcc_r_base.default : sample=0% (threshold=3%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -171,17 +171,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 505.mcf_r,mcf_r_base.default : sample=0% (threshold=3%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -195,17 +195,17 @@ 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(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking exe.regression : 507.cactuBSSN_r,cactusBSSN_r_base.default : sample=0% (threshold=3%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking exe.regression : 507.cactuBSSN_r,cactusBSSN_r_base.default : sample=1% (threshold=3%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -222,17 +222,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 508.namd_r,namd_r_base.default : sample=0% (threshold=3%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -249,17 +249,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 510.parest_r,parest_r_base.default : sample=0% (threshold=3%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -276,17 +276,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 511.povray_r,povray_r_base.default : sample=0% (threshold=3%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -303,17 +303,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 519.lbm_r,lbm_r_base.default : sample=0% (threshold=3%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -330,17 +330,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking exe.regression : 520.omnetpp_r,omnetpp_r_base.default : sample=0% (threshold=10.05%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking exe.regression : 520.omnetpp_r,omnetpp_r_base.default : sample=-5% (threshold=10.08%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -357,17 +357,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking exe.regression : 523.xalancbmk_r,cpuxalan_r_base.default : sample=-1% (threshold=3%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking exe.regression : 523.xalancbmk_r,cpuxalan_r_base.default : sample=2% (threshold=3%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -384,17 +384,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 525.x264_r,x264_r_base.default : sample=0% (threshold=3%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -411,17 +411,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 526.blender_r,blender_r_base.default : sample=0% (threshold=3%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -438,17 +438,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 531.deepsjeng_r,deepsjeng_r_base.default : sample=0% (threshold=3%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -465,17 +465,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 538.imagick_r,imagick_r_base.default : sample=0% (threshold=3%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -492,17 +492,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 541.leela_r,leela_r_base.default : sample=0% (threshold=3%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -519,17 +519,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 544.nab_r,nab_r_base.default : sample=-1% (threshold=3.24%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -546,35 +546,35 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 557.xz_r,xz_r_base.default : sample=0% (threshold=3%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(236): f_out.close() +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(245): f_out.close() --- 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(254): output_bmk_results_1(exe_df, "exe", "improvement", None, f_skip, None, run_step_artifacts, metric, mode, details) +output-bmk-results.py(263): output_bmk_results_1(exe_df, "exe", "improvement", None, f_skip, None, run_step_artifacts, metric, mode, details) --- modulename: output-bmk-results, funcname: output_bmk_results_1 -output-bmk-results.py(201): f_out = Outfile("{0}/{1}.{2}".format(run_step_artifacts, sym_type, change_kind), "w", predicate=(details=="verbose")) +output-bmk-results.py(210): f_out = Outfile("{0}/{1}.{2}".format(run_step_artifacts, sym_type, change_kind), "w", predicate=(details=="verbose")) --- modulename: output-bmk-results, funcname: __init__ output-bmk-results.py(19): self.filename=filename output-bmk-results.py(20): self.predicate=predicate output-bmk-results.py(21): if predicate: output-bmk-results.py(22): self.outf = open(filename, mode) output-bmk-results.py(23): self.csvwriter = csv.writer(self.outf) -output-bmk-results.py(203): rel_metric = "rel_" + metric -output-bmk-results.py(204): out_df = results_df[results_df[rel_metric] != -1] -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(212): rel_metric = "rel_" + metric +output-bmk-results.py(213): out_df = results_df[results_df[rel_metric] != -1] +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -591,17 +591,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking exe.improvement : 500.perlbench_r,perlbench_r_base.default : sample=1% (threshold=3%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking exe.improvement : 500.perlbench_r,perlbench_r_base.default : sample=-1% (threshold=3%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -618,17 +618,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking exe.improvement : 502.gcc_r,cpugcc_r_base.default : sample=-3% (threshold=3%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking exe.improvement : 502.gcc_r,cpugcc_r_base.default : sample=0% (threshold=3%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -645,17 +645,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 505.mcf_r,mcf_r_base.default : sample=0% (threshold=3%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -669,17 +669,17 @@ 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(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking exe.improvement : 507.cactuBSSN_r,cactusBSSN_r_base.default : sample=0% (threshold=3%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking exe.improvement : 507.cactuBSSN_r,cactusBSSN_r_base.default : sample=1% (threshold=3%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -696,17 +696,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 508.namd_r,namd_r_base.default : sample=0% (threshold=3%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -723,17 +723,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 510.parest_r,parest_r_base.default : sample=0% (threshold=3%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -750,17 +750,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 511.povray_r,povray_r_base.default : sample=0% (threshold=3%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -777,17 +777,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 519.lbm_r,lbm_r_base.default : sample=0% (threshold=3%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -804,17 +804,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking exe.improvement : 520.omnetpp_r,omnetpp_r_base.default : sample=0% (threshold=10.05%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking exe.improvement : 520.omnetpp_r,omnetpp_r_base.default : sample=-5% (threshold=10.08%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -831,17 +831,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking exe.improvement : 523.xalancbmk_r,cpuxalan_r_base.default : sample=-1% (threshold=3%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking exe.improvement : 523.xalancbmk_r,cpuxalan_r_base.default : sample=2% (threshold=3%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -858,17 +858,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 525.x264_r,x264_r_base.default : sample=0% (threshold=3%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -885,17 +885,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 526.blender_r,blender_r_base.default : sample=0% (threshold=3%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -912,17 +912,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 531.deepsjeng_r,deepsjeng_r_base.default : sample=0% (threshold=3%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -939,17 +939,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 538.imagick_r,imagick_r_base.default : sample=0% (threshold=3%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -966,17 +966,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 541.leela_r,leela_r_base.default : sample=0% (threshold=3%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -993,17 +993,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 544.nab_r,nab_r_base.default : sample=-1% (threshold=3.24%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -1020,35 +1020,35 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 557.xz_r,xz_r_base.default : sample=0% (threshold=3%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(236): f_out.close() +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(245): f_out.close() --- 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(256): output_bmk_results_1(sym_df, "symbol", "regression", f_regr, f_skip, f_ebp, run_step_artifacts, metric, mode, details) +output-bmk-results.py(265): output_bmk_results_1(sym_df, "symbol", "regression", f_regr, f_skip, f_ebp, run_step_artifacts, metric, mode, details) --- modulename: output-bmk-results, funcname: output_bmk_results_1 -output-bmk-results.py(201): f_out = Outfile("{0}/{1}.{2}".format(run_step_artifacts, sym_type, change_kind), "w", predicate=(details=="verbose")) +output-bmk-results.py(210): f_out = Outfile("{0}/{1}.{2}".format(run_step_artifacts, sym_type, change_kind), "w", predicate=(details=="verbose")) --- modulename: output-bmk-results, funcname: __init__ output-bmk-results.py(19): self.filename=filename output-bmk-results.py(20): self.predicate=predicate output-bmk-results.py(21): if predicate: output-bmk-results.py(22): self.outf = open(filename, mode) output-bmk-results.py(23): self.csvwriter = csv.writer(self.outf) -output-bmk-results.py(203): rel_metric = "rel_" + metric -output-bmk-results.py(204): out_df = results_df[results_df[rel_metric] != -1] -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(212): rel_metric = "rel_" + metric +output-bmk-results.py(213): out_df = results_df[results_df[rel_metric] != -1] +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -1065,17 +1065,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.regression : 500.perlbench_r,[.] S_regmatch : sample=3% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.regression : 500.perlbench_r,[.] S_regmatch : sample=1% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -1092,17 +1092,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.regression : 500.perlbench_r,[.] Perl_leave_scope : sample=-7% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.regression : 500.perlbench_r,[.] Perl_leave_scope : sample=3% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -1114,44 +1114,17 @@ output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.regression : 502.gcc_r,libc.so.6 : sample=20% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.regression : 502.gcc_r,libc.so.6 : sample=-6% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) - --- modulename: output-bmk-results, funcname: 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(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.regression : 502.gcc_r,[.] bitmap_ior_into : sample=-4% (threshold=17.4%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): - --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -1168,17 +1141,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.regression : 505.mcf_r,[.] cost_compare : sample=6% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.regression : 505.mcf_r,[.] cost_compare : sample=-3% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -1195,17 +1168,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): 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%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.regression : 505.mcf_r,[.] price_out_impl : sample=-1% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -1222,17 +1195,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.regression : 505.mcf_r,[.] spec_qsort : sample=-1% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.regression : 505.mcf_r,[.] spec_qsort : sample=3% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -1249,17 +1222,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.regression : 505.mcf_r,[.] primal_bea_mpp : sample=-4% (threshold=15.39%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.regression : 505.mcf_r,[.] primal_bea_mpp : sample=2% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -1273,17 +1246,17 @@ 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(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.regression : 507.cactuBSSN_r,[.] _ZL16ML_BSSN_RHS_BodyPK4_cGHiiPKdS3_S3_PKiS5_iPKPd : sample=-2% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.regression : 507.cactuBSSN_r,[.] _ZL16ML_BSSN_RHS_BodyPK4_cGHiiPKdS3_S3_PKiS5_iPKPd : sample=2% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -1297,17 +1270,17 @@ 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(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.regression : 507.cactuBSSN_r,[.] _ZL19ML_BSSN_Advect_BodyPK4_cGHiiPKdS3_S3_PKiS5_iPKPd : sample=1% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.regression : 507.cactuBSSN_r,[.] _ZL19ML_BSSN_Advect_BodyPK4_cGHiiPKdS3_S3_PKiS5_iPKPd : sample=-1% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -1321,17 +1294,17 @@ 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(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.regression : 507.cactuBSSN_r,[.] _ZL24ML_BSSN_constraints_BodyPK4_cGHiiPKdS3_S3_PKiS5_iPKPd : sample=3% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.regression : 507.cactuBSSN_r,[.] _ZL24ML_BSSN_constraints_BodyPK4_cGHiiPKdS3_S3_PKiS5_iPKPd : sample=-2% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -1348,17 +1321,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.regression : 508.namd_r,[.] _Z22pairlist_from_pairlistddddPK8CompAtomPKtiPtdPd : sample=-4% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.regression : 508.namd_r,[.] _Z22pairlist_from_pairlistddddPK8CompAtomPKtiPtdPd : sample=1% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -1375,17 +1348,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.regression : 508.namd_r,[.] _ZN20ComputeNonbondedUtil26calc_pair_energy_fullelectEP9nonbonded : sample=1% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.regression : 508.namd_r,[.] _ZN20ComputeNonbondedUtil26calc_pair_energy_fullelectEP9nonbonded : sample=0% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -1402,17 +1375,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.regression : 508.namd_r,[.] _ZN20ComputeNonbondedUtil19calc_pair_fullelectEP9nonbonded : sample=3% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.regression : 508.namd_r,[.] _ZN20ComputeNonbondedUtil19calc_pair_fullelectEP9nonbonded : sample=-2% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -1429,17 +1402,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.regression : 508.namd_r,[.] _ZN20ComputeNonbondedUtil16calc_pair_energyEP9nonbonded : sample=-3% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.regression : 508.namd_r,[.] _ZN20ComputeNonbondedUtil16calc_pair_energyEP9nonbonded : sample=2% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -1456,17 +1429,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.regression : 508.namd_r,[.] _ZN20ComputeNonbondedUtil32calc_pair_energy_merge_fullelectEP9nonbonded : sample=0% (threshold=19.11%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.regression : 508.namd_r,[.] _ZN20ComputeNonbondedUtil32calc_pair_energy_merge_fullelectEP9nonbonded : sample=-1% (threshold=18.9%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -1483,17 +1456,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.regression : 510.parest_r,[.] _ZNK6dealii9SparseILUIdE5vmultIdEEvRNS_6VectorIT_EERKS5_ : sample=-7% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.regression : 510.parest_r,[.] _ZNK6dealii9SparseILUIdE5vmultIdEEvRNS_6VectorIT_EERKS5_ : sample=3% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -1510,17 +1483,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.regression : 510.parest_r,[.] _ZNK6dealii12SparseMatrixIdE5vmultINS_6VectorIdEES4_EEvRT_RKT0_ : sample=3% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.regression : 510.parest_r,[.] _ZNK6dealii12SparseMatrixIdE5vmultINS_6VectorIdEES4_EEvRT_RKT0_ : sample=0% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -1537,17 +1510,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.regression : 510.parest_r,[.] _ZNK6dealii6VectorIdEmlIdEEdRKNS0_IT_EE : sample=10% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.regression : 510.parest_r,[.] _ZNK6dealii6VectorIdEmlIdEEdRKNS0_IT_EE : sample=-7% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -1564,17 +1537,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.regression : 511.povray_r,[.] _ZN3povL31All_CSG_Intersect_IntersectionsEPNS_13Object_StructEPNS_10Ray_StructEPNS_13istack_structE : sample=-2% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.regression : 511.povray_r,[.] _ZN3povL31All_CSG_Intersect_IntersectionsEPNS_13Object_StructEPNS_10Ray_StructEPNS_13istack_structE : sample=4% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -1591,17 +1564,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.regression : 511.povray_r,[.] _ZN3povL23All_Plane_IntersectionsEPNS_13Object_StructEPNS_10Ray_StructEPNS_13istack_structE : sample=3% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.regression : 511.povray_r,[.] _ZN3povL23All_Plane_IntersectionsEPNS_13Object_StructEPNS_10Ray_StructEPNS_13istack_structE : sample=5% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -1618,17 +1591,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 511.povray_r,[.] _ZN3povL24All_Sphere_IntersectionsEPNS_13Object_StructEPNS_10Ray_StructEPNS_13istack_structE : sample=2% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -1645,17 +1618,47 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.regression : 511.povray_r,[.] _ZN3pov17Check_And_EnqueueEPNS_21Priority_Queue_StructEPNS_16BBox_Tree_StructEPNS_19Bounding_Box_StructEPNS_14Rayinfo_StructE : sample=12% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.regression : 511.povray_r,[.] _ZN3pov17Check_And_EnqueueEPNS_21Priority_Queue_StructEPNS_16BBox_Tree_StructEPNS_19Bounding_Box_StructEPNS_14Rayinfo_StructE : sample=-19% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(227): percent_change, short_diag, long_diag = get_short_long_diag(row, metric, sym_type, change_kind) + --- modulename: output-bmk-results, funcname: get_short_long_diag +output-bmk-results.py(113): bmk = row["benchmark"] +output-bmk-results.py(114): rel_value = row["rel_" + metric] +output-bmk-results.py(115): prev_value = row[metric + "_x"] +output-bmk-results.py(116): curr_value = row[metric + "_y"] +output-bmk-results.py(118): if metric == "sample": +output-bmk-results.py(119): if curr_value == 999999999: +output-bmk-results.py(122): elif curr_value == 888888888: +output-bmk-results.py(125): elif prev_value == 999999999 and curr_value == 888888888: +output-bmk-results.py(128): elif prev_value == 888888888 and curr_value < 888888888: +output-bmk-results.py(131): elif prev_value == 999999999 and curr_value < 888888888: +output-bmk-results.py(144): suffix = "" +output-bmk-results.py(145): if metric == "sample": +output-bmk-results.py(146): prefix_regression = "slowed down by" +output-bmk-results.py(147): prefix_improvement = "sped up by" +output-bmk-results.py(148): suffix = "perf samples" +output-bmk-results.py(159): if sym_type=="symbol": +output-bmk-results.py(160): item=bmk+":"+row["symbol"] +output-bmk-results.py(164): short_diag = "{0} {1} {2}%".format(item, locals()["prefix_" + change_kind], abs(rel_value - 100)) +output-bmk-results.py(165): long_diag = "{0} from {1} to {2} {3}".format(short_diag, prev_value, curr_value, suffix) +output-bmk-results.py(166): return abs(rel_value - 100), short_diag, long_diag +output-bmk-results.py(231): if metric == "sample" \ +output-bmk-results.py(232): and row['symbol_md5sum_x'] == row['symbol_md5sum_y'] \ +output-bmk-results.py(233): and row['symbol_md5sum_x'] != "-1" \ +output-bmk-results.py(234): and row['symbol_md5sum_x'] != "d41d8cd98f00b204e9800998ecf8427e": +output-bmk-results.py(235): f_skip.write_csv((row["benchmark"], row["symbol"], short_diag, long_diag)) + --- modulename: output-bmk-results, funcname: write_csv +output-bmk-results.py(41): if not self.predicate or not self.csvwriter: +output-bmk-results.py(43): self.csvwriter.writerow(arr) +output-bmk-results.py(236): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -1672,17 +1675,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 519.lbm_r,[.] LBM_performStreamCollideTRT : sample=0% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -1699,17 +1702,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.regression : 520.omnetpp_r,[.] _ZN12cMessageHeap11removeFirstEv : sample=0% (threshold=24.450000000000003%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.regression : 520.omnetpp_r,[.] _ZN12cMessageHeap11removeFirstEv : sample=-9% (threshold=24.299999999999997%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -1721,44 +1724,17 @@ output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.regression : 520.omnetpp_r,libc.so.6 : sample=-9% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): - --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) - --- modulename: output-bmk-results, funcname: 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(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.regression : 520.omnetpp_r,[.] _ZN5cGate7deliverEP8cMessage7SimTime : sample=-7% (threshold=15.690000000000001%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.regression : 520.omnetpp_r,libc.so.6 : sample=-6% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -1775,17 +1751,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.regression : 520.omnetpp_r,[.] _ZN11cSimulation16selectNextModuleEv : sample=-2% (threshold=31.259999999999998%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.regression : 520.omnetpp_r,[.] _ZN5cGate7deliverEP8cMessage7SimTime : sample=-5% (threshold=16.62%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -1797,17 +1773,17 @@ output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.regression : 520.omnetpp_r,libstdc++.so.6.0.30 : sample=12% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.regression : 520.omnetpp_r,libstdc++.so.6.0.30 : sample=-11% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -1824,17 +1800,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.regression : 523.xalancbmk_r,[.] _ZN11xalanc_1_1022ReusableArenaAllocatorINS_13XStringCachedEE13destroyObjectEPS1_ : sample=-3% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.regression : 520.omnetpp_r,[.] _ZN11cSimulation16selectNextModuleEv : sample=1% (threshold=31.259999999999998%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -1851,17 +1827,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.regression : 523.xalancbmk_r,[.] _ZN11xalanc_1_1019XalanDOMStringCache7releaseERNS_14XalanDOMStringE : sample=-5% (threshold=24.27%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.regression : 523.xalancbmk_r,[.] _ZN11xalanc_1_1022ReusableArenaAllocatorINS_13XStringCachedEE13destroyObjectEPS1_ : sample=0% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -1878,17 +1854,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.regression : 523.xalancbmk_r,[.] _ZN11xercesc_2_710ValueStore8containsEPKNS_13FieldValueMapE : sample=-1% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.regression : 523.xalancbmk_r,[.] _ZN11xalanc_1_1019XalanDOMStringCache7releaseERNS_14XalanDOMStringE : sample=0% (threshold=24.75%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -1905,17 +1881,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.regression : 525.x264_r,[.] get_ref : sample=9% (threshold=31.11%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.regression : 523.xalancbmk_r,[.] _ZN11xercesc_2_710ValueStore8containsEPKNS_13FieldValueMapE : sample=4% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -1932,47 +1908,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.regression : 525.x264_r,[.] mc_chroma : sample=-25% (threshold=20.43%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.regression : 525.x264_r,[.] get_ref : sample=-8% (threshold=32.730000000000004%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(218): percent_change, short_diag, long_diag = get_short_long_diag(row, metric, sym_type, change_kind) - --- modulename: output-bmk-results, funcname: get_short_long_diag -output-bmk-results.py(113): bmk = row["benchmark"] -output-bmk-results.py(114): rel_value = row["rel_" + metric] -output-bmk-results.py(115): prev_value = row[metric + "_x"] -output-bmk-results.py(116): curr_value = row[metric + "_y"] -output-bmk-results.py(118): if metric == "sample": -output-bmk-results.py(119): if curr_value == 999999999: -output-bmk-results.py(122): elif curr_value == 888888888: -output-bmk-results.py(125): elif prev_value == 999999999 and curr_value == 888888888: -output-bmk-results.py(128): elif prev_value == 888888888 and curr_value < 888888888: -output-bmk-results.py(131): elif prev_value == 999999999 and curr_value < 888888888: -output-bmk-results.py(135): suffix = "" -output-bmk-results.py(136): if metric == "sample": -output-bmk-results.py(137): prefix_regression = "slowed down by" -output-bmk-results.py(138): prefix_improvement = "sped up by" -output-bmk-results.py(139): suffix = "perf samples" -output-bmk-results.py(150): if sym_type=="symbol": -output-bmk-results.py(151): item=bmk+":"+row["symbol"] -output-bmk-results.py(155): short_diag = "{0} {1} {2}%".format(item, locals()["prefix_" + change_kind], abs(rel_value - 100)) -output-bmk-results.py(156): long_diag = "{0} from {1} to {2} {3}".format(short_diag, prev_value, curr_value, suffix) -output-bmk-results.py(157): return abs(rel_value - 100), short_diag, long_diag -output-bmk-results.py(222): if metric == "sample" \ -output-bmk-results.py(223): and row['symbol_md5sum_x'] == row['symbol_md5sum_y'] \ -output-bmk-results.py(224): and row['symbol_md5sum_x'] != "-1" \ -output-bmk-results.py(225): and row['symbol_md5sum_x'] != "d41d8cd98f00b204e9800998ecf8427e": -output-bmk-results.py(226): 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(227): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -1989,17 +1935,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.regression : 525.x264_r,[.] x264_pixel_satd_16x16 : sample=10% (threshold=32.07%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.regression : 525.x264_r,[.] x264_pixel_satd_16x16 : sample=-3% (threshold=31.86%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -2016,17 +1962,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.regression : 525.x264_r,[.] x264_pixel_satd_8x8 : sample=15% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.regression : 525.x264_r,[.] x264_pixel_satd_8x8 : sample=-1% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -2043,17 +1989,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.regression : 526.blender_r,[.] _ZL9intersectILi1024EEiP8VBVHTreeP5Isect : sample=-4% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.regression : 526.blender_r,[.] _ZL9intersectILi1024EEiP8VBVHTreeP5Isect : sample=0% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -2070,17 +2016,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.regression : 526.blender_r,[.] RE_rayobject_intersect : sample=8% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.regression : 526.blender_r,[.] RE_rayobject_intersect : sample=5% (threshold=15.03%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -2097,17 +2043,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): 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=-3% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.regression : 531.deepsjeng_r,[.] _Z7ProbeTTP7state_tPiiiPjS1_S1_S1_S1_i : sample=4% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -2124,17 +2070,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.regression : 531.deepsjeng_r,[.] _Z5fevalP7state_tiP12t_eval_comps : sample=-8% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.regression : 531.deepsjeng_r,[.] _Z5fevalP7state_tiP12t_eval_comps : sample=-5% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -2151,17 +2097,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 538.imagick_r,[.] MorphologyApply : sample=0% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -2178,17 +2124,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.regression : 538.imagick_r,[.] MeanShiftImage : sample=2% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.regression : 538.imagick_r,[.] MeanShiftImage : sample=1% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -2205,17 +2151,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.regression : 541.leela_r,[.] _ZN9FastBoard10self_atariEii : sample=-4% (threshold=16.47%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.regression : 541.leela_r,[.] _ZN9FastBoard10self_atariEii : sample=0% (threshold=16.35%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -2232,17 +2178,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.regression : 541.leela_r,[.] _ZN9FastState16play_random_moveEi : sample=-6% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.regression : 541.leela_r,[.] _ZN9FastState16play_random_moveEi : sample=0% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -2259,17 +2205,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.regression : 541.leela_r,[.] _ZN9FastState15walk_empty_listEiib : sample=-13% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.regression : 541.leela_r,[.] _ZN9FastState15walk_empty_listEiib : sample=7% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -2286,17 +2232,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.regression : 544.nab_r,[.] mme34 : sample=-1% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.regression : 544.nab_r,[.] mme34 : sample=-2% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -2308,17 +2254,17 @@ output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.regression : 544.nab_r,libm.so.6 : sample=2% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.regression : 544.nab_r,libm.so.6 : sample=3% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -2330,44 +2276,17 @@ output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.regression : 544.nab_r,[.] exp@@GLIBC_2.29 : sample=0% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.regression : 544.nab_r,[.] exp@@GLIBC_2.29 : sample=5% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) - --- modulename: output-bmk-results, funcname: 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(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.regression : 544.nab_r,[.] heapsort_pairs : sample=-4% (threshold=15.149999999999999%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): - --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -2384,17 +2303,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.regression : 544.nab_r,[.] nbond : sample=0% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.regression : 544.nab_r,[.] nbond : sample=1% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -2411,17 +2330,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): 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%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.regression : 557.xz_r,[.] lzma_mf_bt4_find : sample=0% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -2438,17 +2357,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.regression : 557.xz_r,[.] lzma_lzma_optimum_normal : sample=-5% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.regression : 557.xz_r,[.] lzma_lzma_optimum_normal : sample=-1% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -2465,35 +2384,35 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): 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=16.169999999999998%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.regression : 557.xz_r,[.] lzma_mf_bt4_skip : sample=1% (threshold=16.47%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression -output-bmk-results.py(166): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(167): return (result - 100 > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(236): f_out.close() +output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(176): return (result - 100 > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(245): f_out.close() --- 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(257): output_bmk_results_1(sym_df, "symbol", "improvement", None, f_skip, None, run_step_artifacts, metric, mode, details) +output-bmk-results.py(266): 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(201): f_out = Outfile("{0}/{1}.{2}".format(run_step_artifacts, sym_type, change_kind), "w", predicate=(details=="verbose")) +output-bmk-results.py(210): f_out = Outfile("{0}/{1}.{2}".format(run_step_artifacts, sym_type, change_kind), "w", predicate=(details=="verbose")) --- modulename: output-bmk-results, funcname: __init__ output-bmk-results.py(19): self.filename=filename output-bmk-results.py(20): self.predicate=predicate output-bmk-results.py(21): if predicate: output-bmk-results.py(22): self.outf = open(filename, mode) output-bmk-results.py(23): self.csvwriter = csv.writer(self.outf) -output-bmk-results.py(203): rel_metric = "rel_" + metric -output-bmk-results.py(204): out_df = results_df[results_df[rel_metric] != -1] -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(212): rel_metric = "rel_" + metric +output-bmk-results.py(213): out_df = results_df[results_df[rel_metric] != -1] +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -2510,17 +2429,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.improvement : 500.perlbench_r,[.] S_regmatch : sample=3% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.improvement : 500.perlbench_r,[.] S_regmatch : sample=1% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -2537,17 +2456,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.improvement : 500.perlbench_r,[.] Perl_leave_scope : sample=-7% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.improvement : 500.perlbench_r,[.] Perl_leave_scope : sample=3% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -2559,48 +2478,17 @@ output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.improvement : 502.gcc_r,libc.so.6 : sample=20% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.improvement : 502.gcc_r,libc.so.6 : sample=-6% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(218): percent_change, short_diag, long_diag = get_short_long_diag(row, metric, sym_type, change_kind) - --- modulename: output-bmk-results, funcname: get_short_long_diag -output-bmk-results.py(113): bmk = row["benchmark"] -output-bmk-results.py(114): rel_value = row["rel_" + metric] -output-bmk-results.py(115): prev_value = row[metric + "_x"] -output-bmk-results.py(116): curr_value = row[metric + "_y"] -output-bmk-results.py(118): if metric == "sample": -output-bmk-results.py(119): if curr_value == 999999999: -output-bmk-results.py(122): elif curr_value == 888888888: -output-bmk-results.py(125): elif prev_value == 999999999 and curr_value == 888888888: -output-bmk-results.py(128): elif prev_value == 888888888 and curr_value < 888888888: -output-bmk-results.py(131): elif prev_value == 999999999 and curr_value < 888888888: -output-bmk-results.py(135): suffix = "" -output-bmk-results.py(136): if metric == "sample": -output-bmk-results.py(137): prefix_regression = "slowed down by" -output-bmk-results.py(138): prefix_improvement = "sped up by" -output-bmk-results.py(139): suffix = "perf samples" -output-bmk-results.py(150): if sym_type=="symbol": -output-bmk-results.py(151): item=bmk+":"+row["symbol"] -output-bmk-results.py(155): short_diag = "{0} {1} {2}%".format(item, locals()["prefix_" + change_kind], abs(rel_value - 100)) -output-bmk-results.py(156): long_diag = "{0} from {1} to {2} {3}".format(short_diag, prev_value, curr_value, suffix) -output-bmk-results.py(157): return abs(rel_value - 100), short_diag, long_diag -output-bmk-results.py(222): if metric == "sample" \ -output-bmk-results.py(223): and row['symbol_md5sum_x'] == row['symbol_md5sum_y'] \ -output-bmk-results.py(224): and row['symbol_md5sum_x'] != "-1" \ -output-bmk-results.py(229): print("DEBUG: *** {0},{1} : {2}".format(row["benchmark"], row["symbol"], long_diag)) -DEBUG: *** 502.gcc_r,libc.so.6 : 502.gcc_r:libc.so.6 sped up by 20% from 181 to 144 perf samples -output-bmk-results.py(231): 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(232): if change_kind == "regression": -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -2617,17 +2505,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.improvement : 502.gcc_r,[.] bitmap_ior_into : sample=-4% (threshold=17.4%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.improvement : 505.mcf_r,[.] cost_compare : sample=-3% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -2644,17 +2532,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.improvement : 505.mcf_r,[.] cost_compare : sample=6% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.improvement : 505.mcf_r,[.] price_out_impl : sample=-1% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -2671,17 +2559,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): 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%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.improvement : 505.mcf_r,[.] spec_qsort : sample=3% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -2698,44 +2586,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.improvement : 505.mcf_r,[.] spec_qsort : sample=-1% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.improvement : 505.mcf_r,[.] primal_bea_mpp : sample=2% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) - --- modulename: output-bmk-results, funcname: 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(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.improvement : 505.mcf_r,[.] primal_bea_mpp : sample=-4% (threshold=15.39%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): - --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -2749,17 +2610,17 @@ 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(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.improvement : 507.cactuBSSN_r,[.] _ZL16ML_BSSN_RHS_BodyPK4_cGHiiPKdS3_S3_PKiS5_iPKPd : sample=-2% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.improvement : 507.cactuBSSN_r,[.] _ZL16ML_BSSN_RHS_BodyPK4_cGHiiPKdS3_S3_PKiS5_iPKPd : sample=2% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -2773,17 +2634,17 @@ 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(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.improvement : 507.cactuBSSN_r,[.] _ZL19ML_BSSN_Advect_BodyPK4_cGHiiPKdS3_S3_PKiS5_iPKPd : sample=1% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.improvement : 507.cactuBSSN_r,[.] _ZL19ML_BSSN_Advect_BodyPK4_cGHiiPKdS3_S3_PKiS5_iPKPd : sample=-1% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -2797,17 +2658,17 @@ 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(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.improvement : 507.cactuBSSN_r,[.] _ZL24ML_BSSN_constraints_BodyPK4_cGHiiPKdS3_S3_PKiS5_iPKPd : sample=3% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.improvement : 507.cactuBSSN_r,[.] _ZL24ML_BSSN_constraints_BodyPK4_cGHiiPKdS3_S3_PKiS5_iPKPd : sample=-2% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -2824,17 +2685,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.improvement : 508.namd_r,[.] _Z22pairlist_from_pairlistddddPK8CompAtomPKtiPtdPd : sample=-4% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.improvement : 508.namd_r,[.] _Z22pairlist_from_pairlistddddPK8CompAtomPKtiPtdPd : sample=1% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -2851,17 +2712,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.improvement : 508.namd_r,[.] _ZN20ComputeNonbondedUtil26calc_pair_energy_fullelectEP9nonbonded : sample=1% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.improvement : 508.namd_r,[.] _ZN20ComputeNonbondedUtil26calc_pair_energy_fullelectEP9nonbonded : sample=0% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -2878,17 +2739,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.improvement : 508.namd_r,[.] _ZN20ComputeNonbondedUtil19calc_pair_fullelectEP9nonbonded : sample=3% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.improvement : 508.namd_r,[.] _ZN20ComputeNonbondedUtil19calc_pair_fullelectEP9nonbonded : sample=-2% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -2905,17 +2766,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.improvement : 508.namd_r,[.] _ZN20ComputeNonbondedUtil16calc_pair_energyEP9nonbonded : sample=-3% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.improvement : 508.namd_r,[.] _ZN20ComputeNonbondedUtil16calc_pair_energyEP9nonbonded : sample=2% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -2932,17 +2793,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.improvement : 508.namd_r,[.] _ZN20ComputeNonbondedUtil32calc_pair_energy_merge_fullelectEP9nonbonded : sample=0% (threshold=19.11%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.improvement : 508.namd_r,[.] _ZN20ComputeNonbondedUtil32calc_pair_energy_merge_fullelectEP9nonbonded : sample=-1% (threshold=18.9%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -2959,17 +2820,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.improvement : 510.parest_r,[.] _ZNK6dealii9SparseILUIdE5vmultIdEEvRNS_6VectorIT_EERKS5_ : sample=-7% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.improvement : 510.parest_r,[.] _ZNK6dealii9SparseILUIdE5vmultIdEEvRNS_6VectorIT_EERKS5_ : sample=3% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -2986,17 +2847,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.improvement : 510.parest_r,[.] _ZNK6dealii12SparseMatrixIdE5vmultINS_6VectorIdEES4_EEvRT_RKT0_ : sample=3% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.improvement : 510.parest_r,[.] _ZNK6dealii12SparseMatrixIdE5vmultINS_6VectorIdEES4_EEvRT_RKT0_ : sample=0% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -3013,17 +2874,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.improvement : 510.parest_r,[.] _ZNK6dealii6VectorIdEmlIdEEdRKNS0_IT_EE : sample=10% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.improvement : 510.parest_r,[.] _ZNK6dealii6VectorIdEmlIdEEdRKNS0_IT_EE : sample=-7% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -3040,17 +2901,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.improvement : 511.povray_r,[.] _ZN3povL31All_CSG_Intersect_IntersectionsEPNS_13Object_StructEPNS_10Ray_StructEPNS_13istack_structE : sample=-2% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.improvement : 511.povray_r,[.] _ZN3povL31All_CSG_Intersect_IntersectionsEPNS_13Object_StructEPNS_10Ray_StructEPNS_13istack_structE : sample=4% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -3067,17 +2928,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.improvement : 511.povray_r,[.] _ZN3povL23All_Plane_IntersectionsEPNS_13Object_StructEPNS_10Ray_StructEPNS_13istack_structE : sample=3% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.improvement : 511.povray_r,[.] _ZN3povL23All_Plane_IntersectionsEPNS_13Object_StructEPNS_10Ray_StructEPNS_13istack_structE : sample=5% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -3094,17 +2955,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 511.povray_r,[.] _ZN3povL24All_Sphere_IntersectionsEPNS_13Object_StructEPNS_10Ray_StructEPNS_13istack_structE : sample=2% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -3121,17 +2982,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.improvement : 511.povray_r,[.] _ZN3pov17Check_And_EnqueueEPNS_21Priority_Queue_StructEPNS_16BBox_Tree_StructEPNS_19Bounding_Box_StructEPNS_14Rayinfo_StructE : sample=12% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.improvement : 511.povray_r,[.] _ZN3pov17Check_And_EnqueueEPNS_21Priority_Queue_StructEPNS_16BBox_Tree_StructEPNS_19Bounding_Box_StructEPNS_14Rayinfo_StructE : sample=-19% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -3148,17 +3009,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 519.lbm_r,[.] LBM_performStreamCollideTRT : sample=0% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -3175,17 +3036,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.improvement : 520.omnetpp_r,[.] _ZN12cMessageHeap11removeFirstEv : sample=0% (threshold=24.450000000000003%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.improvement : 520.omnetpp_r,[.] _ZN12cMessageHeap11removeFirstEv : sample=-9% (threshold=24.299999999999997%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -3197,17 +3058,17 @@ output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.improvement : 520.omnetpp_r,libc.so.6 : sample=-9% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.improvement : 520.omnetpp_r,libc.so.6 : sample=-6% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -3224,44 +3085,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.improvement : 520.omnetpp_r,[.] _ZN5cGate7deliverEP8cMessage7SimTime : sample=-7% (threshold=15.690000000000001%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.improvement : 520.omnetpp_r,[.] _ZN5cGate7deliverEP8cMessage7SimTime : sample=-5% (threshold=16.62%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) - --- modulename: output-bmk-results, funcname: 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(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.improvement : 520.omnetpp_r,[.] _ZN11cSimulation16selectNextModuleEv : sample=-2% (threshold=31.259999999999998%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): - --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -3273,17 +3107,17 @@ output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.improvement : 520.omnetpp_r,libstdc++.so.6.0.30 : sample=12% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.improvement : 520.omnetpp_r,libstdc++.so.6.0.30 : sample=-11% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -3300,17 +3134,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.improvement : 523.xalancbmk_r,[.] _ZN11xalanc_1_1022ReusableArenaAllocatorINS_13XStringCachedEE13destroyObjectEPS1_ : sample=-3% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.improvement : 520.omnetpp_r,[.] _ZN11cSimulation16selectNextModuleEv : sample=1% (threshold=31.259999999999998%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -3327,17 +3161,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.improvement : 523.xalancbmk_r,[.] _ZN11xalanc_1_1019XalanDOMStringCache7releaseERNS_14XalanDOMStringE : sample=-5% (threshold=24.27%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.improvement : 523.xalancbmk_r,[.] _ZN11xalanc_1_1022ReusableArenaAllocatorINS_13XStringCachedEE13destroyObjectEPS1_ : sample=0% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -3354,17 +3188,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.improvement : 523.xalancbmk_r,[.] _ZN11xercesc_2_710ValueStore8containsEPKNS_13FieldValueMapE : sample=-1% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.improvement : 523.xalancbmk_r,[.] _ZN11xalanc_1_1019XalanDOMStringCache7releaseERNS_14XalanDOMStringE : sample=0% (threshold=24.75%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -3381,17 +3215,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.improvement : 525.x264_r,[.] get_ref : sample=9% (threshold=31.11%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.improvement : 523.xalancbmk_r,[.] _ZN11xercesc_2_710ValueStore8containsEPKNS_13FieldValueMapE : sample=4% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -3408,17 +3242,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.improvement : 525.x264_r,[.] mc_chroma : sample=-25% (threshold=20.43%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.improvement : 525.x264_r,[.] get_ref : sample=-8% (threshold=32.730000000000004%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -3435,17 +3269,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.improvement : 525.x264_r,[.] x264_pixel_satd_16x16 : sample=10% (threshold=32.07%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.improvement : 525.x264_r,[.] x264_pixel_satd_16x16 : sample=-3% (threshold=31.86%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -3462,17 +3296,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.improvement : 525.x264_r,[.] x264_pixel_satd_8x8 : sample=15% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.improvement : 525.x264_r,[.] x264_pixel_satd_8x8 : sample=-1% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -3489,17 +3323,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.improvement : 526.blender_r,[.] _ZL9intersectILi1024EEiP8VBVHTreeP5Isect : sample=-4% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.improvement : 526.blender_r,[.] _ZL9intersectILi1024EEiP8VBVHTreeP5Isect : sample=0% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -3516,17 +3350,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.improvement : 526.blender_r,[.] RE_rayobject_intersect : sample=8% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.improvement : 526.blender_r,[.] RE_rayobject_intersect : sample=5% (threshold=15.03%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -3543,17 +3377,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): 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=-3% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.improvement : 531.deepsjeng_r,[.] _Z7ProbeTTP7state_tPiiiPjS1_S1_S1_S1_i : sample=4% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -3570,17 +3404,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.improvement : 531.deepsjeng_r,[.] _Z5fevalP7state_tiP12t_eval_comps : sample=-8% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.improvement : 531.deepsjeng_r,[.] _Z5fevalP7state_tiP12t_eval_comps : sample=-5% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -3597,17 +3431,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 538.imagick_r,[.] MorphologyApply : sample=0% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -3624,17 +3458,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.improvement : 538.imagick_r,[.] MeanShiftImage : sample=2% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.improvement : 538.imagick_r,[.] MeanShiftImage : sample=1% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -3651,17 +3485,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.improvement : 541.leela_r,[.] _ZN9FastBoard10self_atariEii : sample=-4% (threshold=16.47%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.improvement : 541.leela_r,[.] _ZN9FastBoard10self_atariEii : sample=0% (threshold=16.35%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -3678,17 +3512,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.improvement : 541.leela_r,[.] _ZN9FastState16play_random_moveEi : sample=-6% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.improvement : 541.leela_r,[.] _ZN9FastState16play_random_moveEi : sample=0% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -3705,17 +3539,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.improvement : 541.leela_r,[.] _ZN9FastState15walk_empty_listEiib : sample=-13% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.improvement : 541.leela_r,[.] _ZN9FastState15walk_empty_listEiib : sample=7% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -3732,17 +3566,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.improvement : 544.nab_r,[.] mme34 : sample=-1% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.improvement : 544.nab_r,[.] mme34 : sample=-2% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -3754,17 +3588,17 @@ output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.improvement : 544.nab_r,libm.so.6 : sample=2% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.improvement : 544.nab_r,libm.so.6 : sample=3% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -3776,17 +3610,17 @@ output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.improvement : 544.nab_r,[.] exp@@GLIBC_2.29 : sample=0% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.improvement : 544.nab_r,[.] exp@@GLIBC_2.29 : sample=5% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -3803,17 +3637,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.improvement : 544.nab_r,[.] heapsort_pairs : sample=-4% (threshold=15.149999999999999%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.improvement : 544.nab_r,[.] nbond : sample=1% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -3830,17 +3664,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.improvement : 544.nab_r,[.] nbond : sample=0% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.improvement : 557.xz_r,[.] lzma_mf_bt4_find : sample=0% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -3857,17 +3691,17 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): 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%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.improvement : 557.xz_r,[.] lzma_lzma_optimum_normal : sample=-1% (threshold=15%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(218): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) @@ -3884,66 +3718,40 @@ output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -DEBUG: checking symbol.improvement : 557.xz_r,[.] lzma_lzma_optimum_normal : sample=-5% (threshold=15%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +output-bmk-results.py(221): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) +output-bmk-results.py(220): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ +DEBUG: checking symbol.improvement : 557.xz_r,[.] lzma_mf_bt4_skip : sample=1% (threshold=16.47%) +output-bmk-results.py(224): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(209): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) - --- modulename: output-bmk-results, funcname: 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(211): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ -output-bmk-results.py(212): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) -output-bmk-results.py(211): 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=16.169999999999998%) -output-bmk-results.py(215): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): - --- modulename: output-bmk-results, funcname: is_entry_improvement -output-bmk-results.py(175): if metric in metric_utils.higher_regress_metrics: -output-bmk-results.py(176): return (100 - result > threshold) -output-bmk-results.py(216): continue -output-bmk-results.py(207): for index, row in out_df.iterrows(): -output-bmk-results.py(236): f_out.close() +output-bmk-results.py(184): if metric in metric_utils.higher_regress_metrics: +output-bmk-results.py(185): return (100 - result > threshold) +output-bmk-results.py(225): continue +output-bmk-results.py(216): for index, row in out_df.iterrows(): +output-bmk-results.py(245): f_out.close() --- 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(259): f_ebp.write("\n") +output-bmk-results.py(33): os.remove(self.filename) +output-bmk-results.py(268): f_ebp.write("\n") --- modulename: output-bmk-results, funcname: write output-bmk-results.py(36): if not self.predicate or not self.outf: output-bmk-results.py(38): self.outf.write(string) -output-bmk-results.py(261): f_skip.close() +output-bmk-results.py(270): f_skip.close() --- modulename: output-bmk-results, funcname: close output-bmk-results.py(29): if not self.outf: output-bmk-results.py(31): self.outf.close() output-bmk-results.py(32): if os.stat(self.filename).st_size == 0: -output-bmk-results.py(262): f_regr.close() +output-bmk-results.py(271): 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(263): f_ebp.close() +output-bmk-results.py(272): f_ebp.close() --- modulename: output-bmk-results, funcname: close output-bmk-results.py(29): if not self.outf: output-bmk-results.py(31): self.outf.close() output-bmk-results.py(32): if os.stat(self.filename).st_size == 0: -output-bmk-results.py(288): return 0 +output-bmk-results.py(297): return 0 |