--- modulename: output-bmk-results, funcname: (1): --- modulename: output-bmk-results, funcname: main output-bmk-results.py(322): results_csv = sys.argv[1] output-bmk-results.py(323): variability_file = sys.argv[2] output-bmk-results.py(324): run_step_artifacts_dir = sys.argv[3] output-bmk-results.py(325): metric = sys.argv[4] output-bmk-results.py(326): mode = sys.argv[5] output-bmk-results.py(327): details = sys.argv[6] output-bmk-results.py(329): merged_df = read_results_csv(results_csv) --- modulename: output-bmk-results, funcname: read_results_csv output-bmk-results.py(312): df = pd.read_csv(results_csv) output-bmk-results.py(313): df = df.fillna(-1) output-bmk-results.py(315): for metric in get_comparable_metrics(df): --- modulename: output-bmk-results, funcname: get_comparable_metrics output-bmk-results.py(206): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ --- modulename: output-bmk-results, funcname: output-bmk-results.py(206): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ --- modulename: output-bmk-results, funcname: output-bmk-results.py(206): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ --- modulename: output-bmk-results, funcname: output-bmk-results.py(206): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ --- modulename: output-bmk-results, funcname: output-bmk-results.py(206): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ --- modulename: output-bmk-results, funcname: output-bmk-results.py(206): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ --- modulename: output-bmk-results, funcname: output-bmk-results.py(206): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ --- modulename: output-bmk-results, funcname: output-bmk-results.py(206): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ output-bmk-results.py(206): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ output-bmk-results.py(206): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ output-bmk-results.py(206): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ output-bmk-results.py(206): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ output-bmk-results.py(206): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ output-bmk-results.py(206): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ output-bmk-results.py(206): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ output-bmk-results.py(206): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ output-bmk-results.py(206): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ output-bmk-results.py(206): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ output-bmk-results.py(206): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ output-bmk-results.py(206): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ output-bmk-results.py(207): & metric_utils.comparable_metrics output-bmk-results.py(206): return set(metric[len("rel_"):] for metric in df.columns[2:] if metric.startswith("rel_")) \ output-bmk-results.py(316): df["rel_" + metric] = df["rel_" + metric].astype("int") output-bmk-results.py(317): df[metric + "_x"] = df[metric + "_x"].astype("int") output-bmk-results.py(318): df[metric + "_y"] = df[metric + "_y"].astype("int") output-bmk-results.py(315): for metric in get_comparable_metrics(df): output-bmk-results.py(316): df["rel_" + metric] = df["rel_" + metric].astype("int") output-bmk-results.py(317): df[metric + "_x"] = df[metric + "_x"].astype("int") output-bmk-results.py(318): df[metric + "_y"] = df[metric + "_y"].astype("int") output-bmk-results.py(315): for metric in get_comparable_metrics(df): output-bmk-results.py(316): df["rel_" + metric] = df["rel_" + metric].astype("int") output-bmk-results.py(317): df[metric + "_x"] = df[metric + "_x"].astype("int") output-bmk-results.py(318): df[metric + "_y"] = df[metric + "_y"].astype("int") output-bmk-results.py(315): for metric in get_comparable_metrics(df): output-bmk-results.py(316): df["rel_" + metric] = df["rel_" + metric].astype("int") output-bmk-results.py(317): df[metric + "_x"] = df[metric + "_x"].astype("int") output-bmk-results.py(318): df[metric + "_y"] = df[metric + "_y"].astype("int") output-bmk-results.py(315): for metric in get_comparable_metrics(df): output-bmk-results.py(319): return df output-bmk-results.py(330): read_specific_variability_file(variability_file) --- modulename: output-bmk-results, funcname: read_specific_variability_file output-bmk-results.py(51): if not os.path.exists(bmk_specific_filename): output-bmk-results.py(52): return None output-bmk-results.py(331): output_bmk_results(merged_df, run_step_artifacts_dir, metric, mode, details) --- modulename: output-bmk-results, funcname: output_bmk_results output-bmk-results.py(278): f_regr = Outfile("{0}/results.regressions".format(run_step_artifacts), "w") --- modulename: output-bmk-results, funcname: __init__ output-bmk-results.py(19): self.filename=filename output-bmk-results.py(20): self.predicate=predicate output-bmk-results.py(21): if predicate: output-bmk-results.py(22): self.outf = open(filename, mode) output-bmk-results.py(23): self.csvwriter = csv.writer(self.outf) output-bmk-results.py(279): f_ebp = Outfile("{0}/extra-bisect-params".format(run_step_artifacts), "w") --- modulename: output-bmk-results, funcname: __init__ output-bmk-results.py(19): self.filename=filename output-bmk-results.py(20): self.predicate=predicate output-bmk-results.py(21): if predicate: output-bmk-results.py(22): self.outf = open(filename, mode) output-bmk-results.py(23): self.csvwriter = csv.writer(self.outf) output-bmk-results.py(280): f_skip = Outfile("{0}/any.skipped".format(run_step_artifacts), "w", predicate=(details=="verbose")) --- modulename: output-bmk-results, funcname: __init__ output-bmk-results.py(19): self.filename=filename output-bmk-results.py(20): self.predicate=predicate output-bmk-results.py(21): if predicate: output-bmk-results.py(22): self.outf = open(filename, mode) output-bmk-results.py(23): self.csvwriter = csv.writer(self.outf) output-bmk-results.py(282): f_ebp.write("extra_build_params=") --- modulename: output-bmk-results, funcname: write output-bmk-results.py(36): if not self.predicate or not self.outf: output-bmk-results.py(38): self.outf.write(string) output-bmk-results.py(286): df = merged_df[merged_df["benchmark"] != "Mean"] output-bmk-results.py(289): exe_df = df[df["symbol"].str.endswith("_base.default")] output-bmk-results.py(290): sym_df = df[~df["symbol"].str.endswith("_base.default")] output-bmk-results.py(293): output_bmk_results_status(exe_df, "regression", f_regr, f_ebp, run_step_artifacts, details) --- modulename: output-bmk-results, funcname: output_bmk_results_status output-bmk-results.py(256): f_out = Outfile("{0}/status.{1}".format(run_step_artifacts, change_kind), "w", predicate=(details=="verbose")) --- modulename: output-bmk-results, funcname: __init__ output-bmk-results.py(19): self.filename=filename output-bmk-results.py(20): self.predicate=predicate output-bmk-results.py(21): if predicate: output-bmk-results.py(22): self.outf = open(filename, mode) output-bmk-results.py(23): self.csvwriter = csv.writer(self.outf) output-bmk-results.py(258): print(results_df) benchmark ... status_y 0 400.perlbench ... failed-to-run 1 401.bzip2 ... success 7 403.gcc ... failed-to-run 8 403.gcc ... failed-to-run 13 429.mcf ... success 17 433.milc ... -1 18 433.milc ... failed-to-run 22 444.namd ... failed-to-run 23 445.gobmk ... failed-to-run 24 447.dealII ... failed-to-run 25 450.soplex ... failed-to-run 26 450.soplex ... failed-to-run 31 453.povray ... failed-to-run 32 456.hmmer ... failed-to-run 33 458.sjeng ... success 38 462.libquantum ... success 42 464.h264ref ... failed-to-run 44 470.lbm ... failed-to-run 45 470.lbm ... failed-to-run 46 471.omnetpp ... failed-to-run 47 473.astar ... failed-to-run 49 482.sphinx3 ... failed-to-run 54 482.sphinx3 ... failed-to-run 55 483.xalancbmk ... failed-to-run [24 rows x 20 columns] output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(275): f_out.close() --- modulename: output-bmk-results, funcname: close output-bmk-results.py(29): if not self.outf: output-bmk-results.py(31): self.outf.close() output-bmk-results.py(32): if os.stat(self.filename).st_size == 0: output-bmk-results.py(33): os.remove(self.filename) output-bmk-results.py(294): output_bmk_results_status(exe_df, "improvement", None, None, run_step_artifacts, details) --- modulename: output-bmk-results, funcname: output_bmk_results_status output-bmk-results.py(256): f_out = Outfile("{0}/status.{1}".format(run_step_artifacts, change_kind), "w", predicate=(details=="verbose")) --- modulename: output-bmk-results, funcname: __init__ output-bmk-results.py(19): self.filename=filename output-bmk-results.py(20): self.predicate=predicate output-bmk-results.py(21): if predicate: output-bmk-results.py(22): self.outf = open(filename, mode) output-bmk-results.py(23): self.csvwriter = csv.writer(self.outf) output-bmk-results.py(258): print(results_df) benchmark ... status_y 0 400.perlbench ... failed-to-run 1 401.bzip2 ... success 7 403.gcc ... failed-to-run 8 403.gcc ... failed-to-run 13 429.mcf ... success 17 433.milc ... -1 18 433.milc ... failed-to-run 22 444.namd ... failed-to-run 23 445.gobmk ... failed-to-run 24 447.dealII ... failed-to-run 25 450.soplex ... failed-to-run 26 450.soplex ... failed-to-run 31 453.povray ... failed-to-run 32 456.hmmer ... failed-to-run 33 458.sjeng ... success 38 462.libquantum ... success 42 464.h264ref ... failed-to-run 44 470.lbm ... failed-to-run 45 470.lbm ... failed-to-run 46 471.omnetpp ... failed-to-run 47 473.astar ... failed-to-run 49 482.sphinx3 ... failed-to-run 54 482.sphinx3 ... failed-to-run 55 483.xalancbmk ... failed-to-run [24 rows x 20 columns] output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(263): classif, short_diag = get_status_diag(row) --- modulename: output-bmk-results, funcname: get_status_diag output-bmk-results.py(113): bmk = row["benchmark"] output-bmk-results.py(115): short_diag="" output-bmk-results.py(116): classif="" output-bmk-results.py(118): if row["status_x"]!="failed-to-build" and row["status_y"]=="failed-to-build": output-bmk-results.py(121): elif row["status_x"]=="success" and row["status_y"]=="failed-to-run": output-bmk-results.py(124): elif row["status_x"]=="failed-to-build" and row["status_y"]=="failed-to-run": output-bmk-results.py(127): elif row["status_x"]=="failed-to-run" and row["status_y"]=="success": output-bmk-results.py(130): elif row["status_x"]=="failed-to-build" and row["status_y"]=="success": output-bmk-results.py(134): return classif, short_diag output-bmk-results.py(265): if classif != change_kind: output-bmk-results.py(266): continue; output-bmk-results.py(261): for index, row in results_df.iterrows(): output-bmk-results.py(275): f_out.close() --- modulename: output-bmk-results, funcname: close output-bmk-results.py(29): if not self.outf: output-bmk-results.py(31): self.outf.close() output-bmk-results.py(32): if os.stat(self.filename).st_size == 0: output-bmk-results.py(33): os.remove(self.filename) output-bmk-results.py(297): output_bmk_results_1(exe_df, "exe", "regression", f_regr, f_skip, f_ebp, run_step_artifacts, metric, mode, details) --- modulename: output-bmk-results, funcname: output_bmk_results_1 output-bmk-results.py(218): f_out = Outfile("{0}/{1}.{2}".format(run_step_artifacts, sym_type, change_kind), "w", predicate=(details=="verbose")) --- modulename: output-bmk-results, funcname: __init__ output-bmk-results.py(19): self.filename=filename output-bmk-results.py(20): self.predicate=predicate output-bmk-results.py(21): if predicate: output-bmk-results.py(22): self.outf = open(filename, mode) output-bmk-results.py(23): self.csvwriter = csv.writer(self.outf) output-bmk-results.py(220): rel_metric = "rel_" + metric output-bmk-results.py(221): out_df = results_df[results_df[rel_metric] != -1] output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 401.bzip2,bzip2_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 403.gcc,gcc_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 403.gcc,gcc_base.default : sample=1% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 429.mcf,mcf_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 433.milc,milc_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 433.milc,milc_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 450.soplex,soplex_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 450.soplex,soplex_base.default : sample=12% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 458.sjeng,sjeng_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 462.libquantum,libquantum_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 470.lbm,lbm_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 470.lbm,lbm_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 483.xalancbmk,Xalan_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(253): f_out.close() --- modulename: output-bmk-results, funcname: close output-bmk-results.py(29): if not self.outf: output-bmk-results.py(31): self.outf.close() output-bmk-results.py(32): if os.stat(self.filename).st_size == 0: output-bmk-results.py(33): os.remove(self.filename) output-bmk-results.py(298): output_bmk_results_1(exe_df, "exe", "improvement", None, f_skip, None, run_step_artifacts, metric, mode, details) --- modulename: output-bmk-results, funcname: output_bmk_results_1 output-bmk-results.py(218): f_out = Outfile("{0}/{1}.{2}".format(run_step_artifacts, sym_type, change_kind), "w", predicate=(details=="verbose")) --- modulename: output-bmk-results, funcname: __init__ output-bmk-results.py(19): self.filename=filename output-bmk-results.py(20): self.predicate=predicate output-bmk-results.py(21): if predicate: output-bmk-results.py(22): self.outf = open(filename, mode) output-bmk-results.py(23): self.csvwriter = csv.writer(self.outf) output-bmk-results.py(220): rel_metric = "rel_" + metric output-bmk-results.py(221): out_df = results_df[results_df[rel_metric] != -1] output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 401.bzip2,bzip2_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: 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(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 403.gcc,gcc_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: 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(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 403.gcc,gcc_base.default : sample=1% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 429.mcf,mcf_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: 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(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 433.milc,milc_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: 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(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 433.milc,milc_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: 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(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 450.soplex,soplex_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: 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(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 450.soplex,soplex_base.default : sample=12% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(235): percent_change, short_diag, long_diag = get_short_long_diag(row, metric, sym_type, change_kind) --- modulename: output-bmk-results, funcname: get_short_long_diag output-bmk-results.py(137): bmk = row["benchmark"] output-bmk-results.py(139): rel_value = row["rel_" + metric] output-bmk-results.py(140): prev_value = row[metric + "_x"] output-bmk-results.py(141): curr_value = row[metric + "_y"] output-bmk-results.py(142): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(152): suffix = "" output-bmk-results.py(153): if metric == "sample": output-bmk-results.py(154): prefix_regression = "slowed down by" output-bmk-results.py(155): prefix_improvement = "sped up by" output-bmk-results.py(156): suffix = "perf samples" output-bmk-results.py(167): if sym_type=="symbol": output-bmk-results.py(170): item=bmk output-bmk-results.py(172): short_diag = "{1} {2}% - {0}".format(item, locals()["prefix_" + change_kind], abs(rel_value - 100)) output-bmk-results.py(173): long_diag = "{0} - from {1} to {2} {3}".format(short_diag, prev_value, curr_value, suffix) output-bmk-results.py(174): return abs(rel_value - 100), short_diag, long_diag output-bmk-results.py(239): if metric == "sample" \ output-bmk-results.py(240): and row['symbol_md5sum_x'] == row['symbol_md5sum_y'] \ output-bmk-results.py(241): and row['symbol_md5sum_x'] != "-1" \ output-bmk-results.py(242): and row['symbol_md5sum_x'] != "d41d8cd98f00b204e9800998ecf8427e": output-bmk-results.py(243): f_skip.write_csv((row["benchmark"], row["symbol"], short_diag, long_diag)) --- modulename: output-bmk-results, funcname: write_csv output-bmk-results.py(41): if not self.predicate or not self.csvwriter: output-bmk-results.py(43): self.csvwriter.writerow(arr) output-bmk-results.py(244): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 458.sjeng,sjeng_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: 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(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 462.libquantum,libquantum_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: 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(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 470.lbm,lbm_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: 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(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 470.lbm,lbm_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: 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(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 483.xalancbmk,Xalan_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(253): f_out.close() --- modulename: output-bmk-results, funcname: close output-bmk-results.py(29): if not self.outf: output-bmk-results.py(31): self.outf.close() output-bmk-results.py(32): if os.stat(self.filename).st_size == 0: output-bmk-results.py(33): os.remove(self.filename) output-bmk-results.py(300): output_bmk_results_1(sym_df, "symbol", "regression", f_regr, f_skip, f_ebp, run_step_artifacts, metric, mode, details) --- modulename: output-bmk-results, funcname: output_bmk_results_1 output-bmk-results.py(218): f_out = Outfile("{0}/{1}.{2}".format(run_step_artifacts, sym_type, change_kind), "w", predicate=(details=="verbose")) --- 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(220): rel_metric = "rel_" + metric output-bmk-results.py(221): out_df = results_df[results_df[rel_metric] != -1] output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 401.bzip2,[.] BZ2_blockSort : sample=-1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 401.bzip2,[.] mainGtU : sample=1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 401.bzip2,[.] fallbackSort : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 401.bzip2,[.] BZ2_compressBlock : sample=-2% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 401.bzip2,[.] BZ2_decompress : sample=-3% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 403.gcc,libc.so.6 : sample=-11% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 403.gcc,[.] memset : sample=-29% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(235): percent_change, short_diag, long_diag = get_short_long_diag(row, metric, sym_type, change_kind) --- modulename: output-bmk-results, funcname: get_short_long_diag output-bmk-results.py(137): bmk = row["benchmark"] output-bmk-results.py(139): rel_value = row["rel_" + metric] output-bmk-results.py(140): prev_value = row[metric + "_x"] output-bmk-results.py(141): curr_value = row[metric + "_y"] output-bmk-results.py(142): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(152): suffix = "" output-bmk-results.py(153): if metric == "sample": output-bmk-results.py(154): prefix_regression = "slowed down by" output-bmk-results.py(155): prefix_improvement = "sped up by" output-bmk-results.py(156): suffix = "perf samples" output-bmk-results.py(167): if sym_type=="symbol": output-bmk-results.py(168): item=bmk+":"+row["symbol"] output-bmk-results.py(172): short_diag = "{1} {2}% - {0}".format(item, locals()["prefix_" + change_kind], abs(rel_value - 100)) output-bmk-results.py(173): long_diag = "{0} - from {1} to {2} {3}".format(short_diag, prev_value, curr_value, suffix) output-bmk-results.py(174): return abs(rel_value - 100), short_diag, long_diag output-bmk-results.py(239): if metric == "sample" \ output-bmk-results.py(240): and row['symbol_md5sum_x'] == row['symbol_md5sum_y'] \ output-bmk-results.py(241): and row['symbol_md5sum_x'] != "-1" \ output-bmk-results.py(242): and row['symbol_md5sum_x'] != "d41d8cd98f00b204e9800998ecf8427e": output-bmk-results.py(243): f_skip.write_csv((row["benchmark"], row["symbol"], short_diag, long_diag)) --- modulename: output-bmk-results, funcname: write_csv output-bmk-results.py(41): if not self.predicate or not self.csvwriter: output-bmk-results.py(43): self.csvwriter.writerow(arr) output-bmk-results.py(244): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 403.gcc,[.] bitmap_operation : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 403.gcc,[.] compute_transp : sample=-8% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 429.mcf,[.] primal_bea_mpp : sample=1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 429.mcf,[.] refresh_potential : sample=-2% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 429.mcf,[.] price_out_impl : sample=-1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 433.milc,[.] make_gather : sample=-25% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(235): percent_change, short_diag, long_diag = get_short_long_diag(row, metric, sym_type, change_kind) --- modulename: output-bmk-results, funcname: get_short_long_diag output-bmk-results.py(137): bmk = row["benchmark"] output-bmk-results.py(139): rel_value = row["rel_" + metric] output-bmk-results.py(140): prev_value = row[metric + "_x"] output-bmk-results.py(141): curr_value = row[metric + "_y"] output-bmk-results.py(142): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(152): suffix = "" output-bmk-results.py(153): if metric == "sample": output-bmk-results.py(154): prefix_regression = "slowed down by" output-bmk-results.py(155): prefix_improvement = "sped up by" output-bmk-results.py(156): suffix = "perf samples" output-bmk-results.py(167): if sym_type=="symbol": output-bmk-results.py(168): item=bmk+":"+row["symbol"] output-bmk-results.py(172): short_diag = "{1} {2}% - {0}".format(item, locals()["prefix_" + change_kind], abs(rel_value - 100)) output-bmk-results.py(173): long_diag = "{0} - from {1} to {2} {3}".format(short_diag, prev_value, curr_value, suffix) output-bmk-results.py(174): return abs(rel_value - 100), short_diag, long_diag output-bmk-results.py(239): if metric == "sample" \ output-bmk-results.py(240): and row['symbol_md5sum_x'] == row['symbol_md5sum_y'] \ output-bmk-results.py(241): and row['symbol_md5sum_x'] != "-1" \ output-bmk-results.py(242): and row['symbol_md5sum_x'] != "d41d8cd98f00b204e9800998ecf8427e": output-bmk-results.py(243): f_skip.write_csv((row["benchmark"], row["symbol"], short_diag, long_diag)) --- modulename: output-bmk-results, funcname: write_csv output-bmk-results.py(41): if not self.predicate or not self.csvwriter: output-bmk-results.py(43): self.csvwriter.writerow(arr) output-bmk-results.py(244): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 444.namd,libc.so.6 : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 444.namd,[.] _IO_fgets : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 450.soplex,libc.so.6 : sample=-20% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(235): percent_change, short_diag, long_diag = get_short_long_diag(row, metric, sym_type, change_kind) --- modulename: output-bmk-results, funcname: get_short_long_diag output-bmk-results.py(137): bmk = row["benchmark"] output-bmk-results.py(139): rel_value = row["rel_" + metric] output-bmk-results.py(140): prev_value = row[metric + "_x"] output-bmk-results.py(141): curr_value = row[metric + "_y"] output-bmk-results.py(142): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(152): suffix = "" output-bmk-results.py(153): if metric == "sample": output-bmk-results.py(154): prefix_regression = "slowed down by" output-bmk-results.py(155): prefix_improvement = "sped up by" output-bmk-results.py(156): suffix = "perf samples" output-bmk-results.py(167): if sym_type=="symbol": output-bmk-results.py(168): item=bmk+":"+row["symbol"] output-bmk-results.py(172): short_diag = "{1} {2}% - {0}".format(item, locals()["prefix_" + change_kind], abs(rel_value - 100)) output-bmk-results.py(173): long_diag = "{0} - from {1} to {2} {3}".format(short_diag, prev_value, curr_value, suffix) output-bmk-results.py(174): return abs(rel_value - 100), short_diag, long_diag output-bmk-results.py(239): if metric == "sample" \ output-bmk-results.py(240): and row['symbol_md5sum_x'] == row['symbol_md5sum_y'] \ output-bmk-results.py(241): and row['symbol_md5sum_x'] != "-1" \ output-bmk-results.py(242): and row['symbol_md5sum_x'] != "d41d8cd98f00b204e9800998ecf8427e": output-bmk-results.py(243): f_skip.write_csv((row["benchmark"], row["symbol"], short_diag, long_diag)) --- modulename: output-bmk-results, funcname: write_csv output-bmk-results.py(41): if not self.predicate or not self.csvwriter: output-bmk-results.py(43): self.csvwriter.writerow(arr) output-bmk-results.py(244): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 450.soplex,[.] _ZN6soplex7NameSet3addERNS_7DataKeyEPKc : sample=-33% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(235): percent_change, short_diag, long_diag = get_short_long_diag(row, metric, sym_type, change_kind) --- modulename: output-bmk-results, funcname: get_short_long_diag output-bmk-results.py(137): bmk = row["benchmark"] output-bmk-results.py(139): rel_value = row["rel_" + metric] output-bmk-results.py(140): prev_value = row[metric + "_x"] output-bmk-results.py(141): curr_value = row[metric + "_y"] output-bmk-results.py(142): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(152): suffix = "" output-bmk-results.py(153): if metric == "sample": output-bmk-results.py(154): prefix_regression = "slowed down by" output-bmk-results.py(155): prefix_improvement = "sped up by" output-bmk-results.py(156): suffix = "perf samples" output-bmk-results.py(167): if sym_type=="symbol": output-bmk-results.py(168): item=bmk+":"+row["symbol"] output-bmk-results.py(172): short_diag = "{1} {2}% - {0}".format(item, locals()["prefix_" + change_kind], abs(rel_value - 100)) output-bmk-results.py(173): long_diag = "{0} - from {1} to {2} {3}".format(short_diag, prev_value, curr_value, suffix) output-bmk-results.py(174): return abs(rel_value - 100), short_diag, long_diag output-bmk-results.py(239): if metric == "sample" \ output-bmk-results.py(240): and row['symbol_md5sum_x'] == row['symbol_md5sum_y'] \ output-bmk-results.py(241): and row['symbol_md5sum_x'] != "-1" \ output-bmk-results.py(242): and row['symbol_md5sum_x'] != "d41d8cd98f00b204e9800998ecf8427e": output-bmk-results.py(243): f_skip.write_csv((row["benchmark"], row["symbol"], short_diag, long_diag)) --- modulename: output-bmk-results, funcname: write_csv output-bmk-results.py(41): if not self.predicate or not self.csvwriter: output-bmk-results.py(43): self.csvwriter.writerow(arr) output-bmk-results.py(244): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 450.soplex,[.] strcmp : sample=33% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 450.soplex,[.] _ZN6soplex23NameSetNameHashFunctionEPKNS_7NameSet4NameE : sample=-50% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(235): percent_change, short_diag, long_diag = get_short_long_diag(row, metric, sym_type, change_kind) --- modulename: output-bmk-results, funcname: get_short_long_diag output-bmk-results.py(137): bmk = row["benchmark"] output-bmk-results.py(139): rel_value = row["rel_" + metric] output-bmk-results.py(140): prev_value = row[metric + "_x"] output-bmk-results.py(141): curr_value = row[metric + "_y"] output-bmk-results.py(142): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(152): suffix = "" output-bmk-results.py(153): if metric == "sample": output-bmk-results.py(154): prefix_regression = "slowed down by" output-bmk-results.py(155): prefix_improvement = "sped up by" output-bmk-results.py(156): suffix = "perf samples" output-bmk-results.py(167): if sym_type=="symbol": output-bmk-results.py(168): item=bmk+":"+row["symbol"] output-bmk-results.py(172): short_diag = "{1} {2}% - {0}".format(item, locals()["prefix_" + change_kind], abs(rel_value - 100)) output-bmk-results.py(173): long_diag = "{0} - from {1} to {2} {3}".format(short_diag, prev_value, curr_value, suffix) output-bmk-results.py(174): return abs(rel_value - 100), short_diag, long_diag output-bmk-results.py(239): if metric == "sample" \ output-bmk-results.py(240): and row['symbol_md5sum_x'] == row['symbol_md5sum_y'] \ output-bmk-results.py(241): and row['symbol_md5sum_x'] != "-1" \ output-bmk-results.py(242): and row['symbol_md5sum_x'] != "d41d8cd98f00b204e9800998ecf8427e": output-bmk-results.py(243): f_skip.write_csv((row["benchmark"], row["symbol"], short_diag, long_diag)) --- modulename: output-bmk-results, funcname: write_csv output-bmk-results.py(41): if not self.predicate or not self.csvwriter: output-bmk-results.py(43): self.csvwriter.writerow(arr) output-bmk-results.py(244): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 458.sjeng,[.] std_eval : sample=4% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 458.sjeng,[.] gen : sample=-6% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 458.sjeng,[.] search : sample=-5% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 458.sjeng,[.] push_slidE : sample=2% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 462.libquantum,[.] quantum_toffoli : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 462.libquantum,[.] quantum_sigma_x : sample=-1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 462.libquantum,[.] quantum_cnot : sample=-3% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 470.lbm,[.] LBM_initializeGrid : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 482.sphinx3,libc.so.6 : sample=38% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 482.sphinx3,[.] __vfscanf_internal : sample=67% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 482.sphinx3,[.] __udivsi3 : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 482.sphinx3,[.] __isoc99_sscanf : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 482.sphinx3,[.] __GI_____strtof_l_internal : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(253): f_out.close() --- modulename: output-bmk-results, funcname: close output-bmk-results.py(29): if not self.outf: output-bmk-results.py(31): self.outf.close() output-bmk-results.py(32): if os.stat(self.filename).st_size == 0: output-bmk-results.py(33): os.remove(self.filename) output-bmk-results.py(301): output_bmk_results_1(sym_df, "symbol", "improvement", None, f_skip, None, run_step_artifacts, metric, mode, details) --- modulename: output-bmk-results, funcname: output_bmk_results_1 output-bmk-results.py(218): f_out = Outfile("{0}/{1}.{2}".format(run_step_artifacts, sym_type, change_kind), "w", predicate=(details=="verbose")) --- 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(220): rel_metric = "rel_" + metric output-bmk-results.py(221): out_df = results_df[results_df[rel_metric] != -1] output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 401.bzip2,[.] BZ2_blockSort : sample=-1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 401.bzip2,[.] mainGtU : sample=1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 401.bzip2,[.] fallbackSort : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 401.bzip2,[.] BZ2_compressBlock : sample=-2% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 401.bzip2,[.] BZ2_decompress : sample=-3% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 403.gcc,libc.so.6 : sample=-11% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 403.gcc,[.] memset : sample=-29% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 403.gcc,[.] bitmap_operation : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 403.gcc,[.] compute_transp : sample=-8% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 429.mcf,[.] primal_bea_mpp : sample=1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 429.mcf,[.] refresh_potential : sample=-2% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 429.mcf,[.] price_out_impl : sample=-1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 433.milc,[.] make_gather : sample=-25% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 444.namd,libc.so.6 : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 444.namd,[.] _IO_fgets : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 450.soplex,libc.so.6 : sample=-20% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 450.soplex,[.] _ZN6soplex7NameSet3addERNS_7DataKeyEPKc : sample=-33% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 450.soplex,[.] strcmp : sample=33% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(235): percent_change, short_diag, long_diag = get_short_long_diag(row, metric, sym_type, change_kind) --- modulename: output-bmk-results, funcname: get_short_long_diag output-bmk-results.py(137): bmk = row["benchmark"] output-bmk-results.py(139): rel_value = row["rel_" + metric] output-bmk-results.py(140): prev_value = row[metric + "_x"] output-bmk-results.py(141): curr_value = row[metric + "_y"] output-bmk-results.py(142): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(152): suffix = "" output-bmk-results.py(153): if metric == "sample": output-bmk-results.py(154): prefix_regression = "slowed down by" output-bmk-results.py(155): prefix_improvement = "sped up by" output-bmk-results.py(156): suffix = "perf samples" output-bmk-results.py(167): if sym_type=="symbol": output-bmk-results.py(168): item=bmk+":"+row["symbol"] output-bmk-results.py(172): short_diag = "{1} {2}% - {0}".format(item, locals()["prefix_" + change_kind], abs(rel_value - 100)) output-bmk-results.py(173): long_diag = "{0} - from {1} to {2} {3}".format(short_diag, prev_value, curr_value, suffix) output-bmk-results.py(174): return abs(rel_value - 100), short_diag, long_diag output-bmk-results.py(239): if metric == "sample" \ output-bmk-results.py(240): and row['symbol_md5sum_x'] == row['symbol_md5sum_y'] \ output-bmk-results.py(241): and row['symbol_md5sum_x'] != "-1" \ output-bmk-results.py(242): and row['symbol_md5sum_x'] != "d41d8cd98f00b204e9800998ecf8427e": output-bmk-results.py(243): f_skip.write_csv((row["benchmark"], row["symbol"], short_diag, long_diag)) --- modulename: output-bmk-results, funcname: write_csv output-bmk-results.py(41): if not self.predicate or not self.csvwriter: output-bmk-results.py(43): self.csvwriter.writerow(arr) output-bmk-results.py(244): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 450.soplex,[.] _ZN6soplex23NameSetNameHashFunctionEPKNS_7NameSet4NameE : sample=-50% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 458.sjeng,[.] std_eval : sample=4% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 458.sjeng,[.] gen : sample=-6% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 458.sjeng,[.] search : sample=-5% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 458.sjeng,[.] push_slidE : sample=2% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 462.libquantum,[.] quantum_toffoli : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 462.libquantum,[.] quantum_sigma_x : sample=-1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 462.libquantum,[.] quantum_cnot : sample=-3% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 470.lbm,[.] LBM_initializeGrid : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 482.sphinx3,libc.so.6 : sample=38% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(235): percent_change, short_diag, long_diag = get_short_long_diag(row, metric, sym_type, change_kind) --- modulename: output-bmk-results, funcname: get_short_long_diag output-bmk-results.py(137): bmk = row["benchmark"] output-bmk-results.py(139): rel_value = row["rel_" + metric] output-bmk-results.py(140): prev_value = row[metric + "_x"] output-bmk-results.py(141): curr_value = row[metric + "_y"] output-bmk-results.py(142): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(152): suffix = "" output-bmk-results.py(153): if metric == "sample": output-bmk-results.py(154): prefix_regression = "slowed down by" output-bmk-results.py(155): prefix_improvement = "sped up by" output-bmk-results.py(156): suffix = "perf samples" output-bmk-results.py(167): if sym_type=="symbol": output-bmk-results.py(168): item=bmk+":"+row["symbol"] output-bmk-results.py(172): short_diag = "{1} {2}% - {0}".format(item, locals()["prefix_" + change_kind], abs(rel_value - 100)) output-bmk-results.py(173): long_diag = "{0} - from {1} to {2} {3}".format(short_diag, prev_value, curr_value, suffix) output-bmk-results.py(174): return abs(rel_value - 100), short_diag, long_diag output-bmk-results.py(239): if metric == "sample" \ output-bmk-results.py(240): and row['symbol_md5sum_x'] == row['symbol_md5sum_y'] \ output-bmk-results.py(241): and row['symbol_md5sum_x'] != "-1" \ output-bmk-results.py(246): print("DEBUG: *** {0},{1} : {2}".format(row["benchmark"], row["symbol"], long_diag)) DEBUG: *** 482.sphinx3,libc.so.6 : sped up by 38% - 482.sphinx3:libc.so.6 - from 8 to 5 perf samples output-bmk-results.py(248): f_out.write_csv((percent_change, row["benchmark"], row["symbol"], short_diag, long_diag)) --- modulename: output-bmk-results, funcname: write_csv output-bmk-results.py(41): if not self.predicate or not self.csvwriter: output-bmk-results.py(43): self.csvwriter.writerow(arr) output-bmk-results.py(249): if change_kind == "regression": output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: 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(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 482.sphinx3,[.] __vfscanf_internal : sample=67% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(235): percent_change, short_diag, long_diag = get_short_long_diag(row, metric, sym_type, change_kind) --- modulename: output-bmk-results, funcname: get_short_long_diag output-bmk-results.py(137): bmk = row["benchmark"] output-bmk-results.py(139): rel_value = row["rel_" + metric] output-bmk-results.py(140): prev_value = row[metric + "_x"] output-bmk-results.py(141): curr_value = row[metric + "_y"] output-bmk-results.py(142): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(152): suffix = "" output-bmk-results.py(153): if metric == "sample": output-bmk-results.py(154): prefix_regression = "slowed down by" output-bmk-results.py(155): prefix_improvement = "sped up by" output-bmk-results.py(156): suffix = "perf samples" output-bmk-results.py(167): if sym_type=="symbol": output-bmk-results.py(168): item=bmk+":"+row["symbol"] output-bmk-results.py(172): short_diag = "{1} {2}% - {0}".format(item, locals()["prefix_" + change_kind], abs(rel_value - 100)) output-bmk-results.py(173): long_diag = "{0} - from {1} to {2} {3}".format(short_diag, prev_value, curr_value, suffix) output-bmk-results.py(174): return abs(rel_value - 100), short_diag, long_diag output-bmk-results.py(239): if metric == "sample" \ output-bmk-results.py(240): and row['symbol_md5sum_x'] == row['symbol_md5sum_y'] \ output-bmk-results.py(241): and row['symbol_md5sum_x'] != "-1" \ output-bmk-results.py(246): print("DEBUG: *** {0},{1} : {2}".format(row["benchmark"], row["symbol"], long_diag)) DEBUG: *** 482.sphinx3,[.] __vfscanf_internal : sped up by 67% - 482.sphinx3:[.] __vfscanf_internal - from 3 to 1 perf samples output-bmk-results.py(248): f_out.write_csv((percent_change, row["benchmark"], row["symbol"], short_diag, long_diag)) --- modulename: output-bmk-results, funcname: write_csv output-bmk-results.py(41): if not self.predicate or not self.csvwriter: output-bmk-results.py(43): self.csvwriter.writerow(arr) output-bmk-results.py(249): if change_kind == "regression": output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: 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(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 482.sphinx3,[.] __udivsi3 : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 482.sphinx3,[.] __isoc99_sscanf : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(58): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 482.sphinx3,[.] __GI_____strtof_l_internal : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(253): f_out.close() --- modulename: output-bmk-results, funcname: close output-bmk-results.py(29): if not self.outf: output-bmk-results.py(31): self.outf.close() output-bmk-results.py(32): if os.stat(self.filename).st_size == 0: output-bmk-results.py(303): f_ebp.write("\n") --- modulename: output-bmk-results, funcname: write output-bmk-results.py(36): if not self.predicate or not self.outf: output-bmk-results.py(38): self.outf.write(string) output-bmk-results.py(305): f_skip.close() --- modulename: output-bmk-results, funcname: close output-bmk-results.py(29): if not self.outf: output-bmk-results.py(31): self.outf.close() output-bmk-results.py(32): if os.stat(self.filename).st_size == 0: output-bmk-results.py(306): f_regr.close() --- modulename: output-bmk-results, funcname: close output-bmk-results.py(29): if not self.outf: output-bmk-results.py(31): self.outf.close() output-bmk-results.py(32): if os.stat(self.filename).st_size == 0: output-bmk-results.py(33): os.remove(self.filename) output-bmk-results.py(307): f_ebp.close() --- modulename: output-bmk-results, funcname: close output-bmk-results.py(29): if not self.outf: 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(332): return 0