--- 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(53): specific_variability = pd.read_csv(bmk_specific_filename, index_col=False) 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 ... success 3 401.bzip2 ... success 9 403.gcc ... success 13 410.bwaves ... success 16 416.gamess ... success 21 429.mcf ... success 24 433.milc ... success 30 434.zeusmp ... success 33 435.gromacs ... success 36 436.cactusADM ... success 38 437.leslie3d ... success 44 444.namd ... success 51 445.gobmk ... success 54 447.dealII ... success 60 450.soplex ... success 64 453.povray ... success 69 454.calculix ... success 72 456.hmmer ... success 74 458.sjeng ... success 78 459.GemsFDTD ... success 84 462.libquantum ... success 88 464.h264ref ... success 93 465.tonto ... success 99 470.lbm ... success 101 471.omnetpp ... success 105 473.astar ... success 109 481.wrf ... success 114 482.sphinx3 ... success 117 483.xalancbmk ... failed-to-build 118 483.xalancbmk ... failed-to-build [30 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(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 ... success 3 401.bzip2 ... success 9 403.gcc ... success 13 410.bwaves ... success 16 416.gamess ... success 21 429.mcf ... success 24 433.milc ... success 30 434.zeusmp ... success 33 435.gromacs ... success 36 436.cactusADM ... success 38 437.leslie3d ... success 44 444.namd ... success 51 445.gobmk ... success 54 447.dealII ... success 60 450.soplex ... success 64 453.povray ... success 69 454.calculix ... success 72 456.hmmer ... success 74 458.sjeng ... success 78 459.GemsFDTD ... success 84 462.libquantum ... success 88 464.h264ref ... success 93 465.tonto ... success 99 470.lbm ... success 101 471.omnetpp ... success 105 473.astar ... success 109 481.wrf ... success 114 482.sphinx3 ... success 117 483.xalancbmk ... failed-to-build 118 483.xalancbmk ... failed-to-build [30 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(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(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 400.perlbench,perlbench_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(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 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(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 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(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 410.bwaves,bwaves_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(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 416.gamess,gamess_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(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 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(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 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(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 434.zeusmp,zeusmp_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(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 435.gromacs,gromacs_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(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 436.cactusADM,cactusADM_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(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 437.leslie3d,leslie3d_base.default : sample=0% (threshold=10.68%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 444.namd,namd_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(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 445.gobmk,gobmk_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(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 447.dealII,dealII_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 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(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 453.povray,povray_base.default : sample=1% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 454.calculix,calculix_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(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 456.hmmer,hmmer_base.default : sample=-1% (threshold=3.99%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 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(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 459.GemsFDTD,GemsFDTD_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 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(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 464.h264ref,h264ref_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 465.tonto,tonto_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(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 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(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 471.omnetpp,omnetpp_base.default : sample=-2% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 473.astar,astar_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(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 481.wrf,wrf_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(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.regression : 482.sphinx3,sphinx_livepretend_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: 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(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 400.perlbench,perlbench_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(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 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(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 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(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 410.bwaves,bwaves_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(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 416.gamess,gamess_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(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 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(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 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(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 434.zeusmp,zeusmp_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(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 435.gromacs,gromacs_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(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 436.cactusADM,cactusADM_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(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 437.leslie3d,leslie3d_base.default : sample=0% (threshold=10.68%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 444.namd,namd_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(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 445.gobmk,gobmk_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(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 447.dealII,dealII_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 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(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 453.povray,povray_base.default : sample=1% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 454.calculix,calculix_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(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 456.hmmer,hmmer_base.default : sample=-1% (threshold=3.99%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 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(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 459.GemsFDTD,GemsFDTD_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 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(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 464.h264ref,h264ref_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 465.tonto,tonto_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(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 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(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 471.omnetpp,omnetpp_base.default : sample=-2% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 473.astar,astar_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(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 481.wrf,wrf_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(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking exe.improvement : 482.sphinx3,sphinx_livepretend_base.default : sample=0% (threshold=3%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: 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(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 400.perlbench,[.] S_regmatch : sample=-1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 400.perlbench,libc.so.6 : sample=-3% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 401.bzip2,[.] mainSort : sample=-2% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 401.bzip2,[.] mainGtU.part.0 : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 401.bzip2,[.] 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(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 401.bzip2,[.] BZ2_compressBlock : sample=1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 401.bzip2,[.] BZ2_decompress : sample=2% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 403.gcc,libc.so.6 : sample=-5% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 403.gcc,[.] reg_is_remote_constant_p : sample=-2% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 403.gcc,[.] memset : sample=-2% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 410.bwaves,[.] mat_times_vec_ : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 410.bwaves,[.] bi_cgstab_block_ : sample=-3% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 416.gamess,[.] forms_ : sample=1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 416.gamess,[.] twotff_ : sample=-1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 416.gamess,[.] dirfck_ : sample=-1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 416.gamess,[.] xyzint_ : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 429.mcf,[.] primal_bea_mpp : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 429.mcf,[.] refresh_potential : sample=-1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 433.milc,[.] mult_su3_na : sample=1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 433.milc,[.] mult_su3_nn : sample=1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 433.milc,[.] mult_su3_mat_vec : sample=-2% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 433.milc,[.] mult_adj_su3_mat_vec : sample=1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 433.milc,[.] uncompress_anti_hermitian : sample=4% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 434.zeusmp,[.] hsmoc_ : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 434.zeusmp,[.] lorentz_ : sample=-1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 435.gromacs,[.] inl1130_ : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 435.gromacs,[.] search_neighbours : sample=1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 436.cactusADM,[.] bench_staggeredleapfrog2_ : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 437.leslie3d,[.] fluxj_ : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 437.leslie3d,[.] fluxk_ : sample=1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 437.leslie3d,[.] fluxi_ : sample=2% (threshold=15.299999999999999%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 437.leslie3d,[.] extrapi_ : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 437.leslie3d,[.] extrapj_ : sample=-2% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 444.namd,[.] _ZN20ComputeNonbondedUtil26calc_pair_energy_fullelectEP9nonbonded : sample=1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 444.namd,[.] _ZN20ComputeNonbondedUtil19calc_pair_fullelectEP9nonbonded : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 444.namd,[.] _ZN20ComputeNonbondedUtil32calc_pair_energy_merge_fullelectEP9nonbonded : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 444.namd,[.] _ZN20ComputeNonbondedUtil16calc_pair_energyEP9nonbonded : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 444.namd,[.] _ZN20ComputeNonbondedUtil25calc_pair_merge_fullelectEP9nonbonded : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 444.namd,[.] _ZN20ComputeNonbondedUtil9calc_pairEP9nonbonded : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 445.gobmk,[.] do_play_move : sample=-3% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 445.gobmk,[.] fastlib : sample=5% (threshold=15.18%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 447.dealII,[.] _ZNK9MappingQ1ILi3EE12compute_fillERK12TriaIteratorILi3E15DoFCellAccessorILi3EEEjN10QProjectorILi3EE17DataSetDescriptorERNS0_12InternalDataERSt6vectorI5PointILi3EESaISE_EE : sample=1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 447.dealII,[.] _ZNK12SparseMatrixIdE5vmultI6VectorIdES3_EEvRT_RKT0_ : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 447.dealII,libstdc++.so.6.0.33 : sample=-3% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 447.dealII,[.] _ZN13LaplaceSolver6SolverILi3EE22assemble_linear_systemERNS1_12LinearSystemE : sample=-2% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 447.dealII,[.] _ZSt18_Rb_tree_incrementPKSt18_Rb_tree_node_base : sample=-6% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 450.soplex,[.] _ZN6soplex10SPxSteepPR8entered4ENS_5SPxIdEi : sample=2% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 450.soplex,[.] _ZN6soplex8SSVector18assign2productFullERKNS_5SVSetERKS0_ : sample=6% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 450.soplex,[.] _ZN6soplex9CLUFactor16initFactorMatrixEPPNS_7SVectorEd : sample=4% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 453.povray,[.] _ZN3povL23All_Plane_IntersectionsEPNS_13Object_StructEPNS_10Ray_StructEPNS_13istack_structE : sample=-2% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 453.povray,[.] _ZN3povL31All_CSG_Intersect_IntersectionsEPNS_13Object_StructEPNS_10Ray_StructEPNS_13istack_structE : sample=1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 453.povray,[.] _ZN3povL24All_Sphere_IntersectionsEPNS_13Object_StructEPNS_10Ray_StructEPNS_13istack_structE : sample=-9% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 453.povray,[.] _ZN3pov17Check_And_EnqueueEPNS_21Priority_Queue_StructEPNS_16BBox_Tree_StructEPNS_19Bounding_Box_StructEPNS_14Rayinfo_StructE : sample=4% (threshold=15.299999999999999%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 454.calculix,[.] e_c3d_ : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 454.calculix,[.] DVdot33 : sample=1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 456.hmmer,[.] P7Viterbi : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 458.sjeng,[.] std_eval : sample=-2% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 458.sjeng,[.] gen : sample=1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 458.sjeng,[.] setup_attackers : sample=2% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 459.GemsFDTD,[.] __upml_mod_MOD_upmlupdatee : sample=-1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 459.GemsFDTD,[.] __nft_mod_MOD_nft_store : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 459.GemsFDTD,[.] __upml_mod_MOD_upmlupdateh : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 459.GemsFDTD,[.] __update_mod_MOD_updateh_homo : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 459.GemsFDTD,[.] __update_mod_MOD_updatee_homo : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 462.libquantum,[.] quantum_toffoli : sample=-2% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 462.libquantum,[.] quantum_sigma_x : sample=5% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 462.libquantum,[.] quantum_cnot : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 464.h264ref,[.] SetupFastFullPelSearch : sample=-1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 464.h264ref,libc.so.6 : sample=-1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 464.h264ref,[.] __memcpy_neon : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 464.h264ref,[.] FastFullPelBlockMotionSearch : sample=-7% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 465.tonto,libm.so.6 : sample=-2% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 465.tonto,[.] __shell2_module_MOD_make_ft_1 : sample=5% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 465.tonto,[.] __sincosl : sample=2% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 465.tonto,[.] __shell1quartet_module_MOD_make_esfs.isra.0 : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 465.tonto,[.] __cexpl : sample=-5% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 470.lbm,[.] LBM_performStreamCollide : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 471.omnetpp,[.] _ZN12cMessageHeap8getFirstEv : sample=-7% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 471.omnetpp,libc.so.6 : sample=1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 471.omnetpp,[.] _ZN5cGate7deliverEP8cMessaged : sample=6% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 473.astar,[.] _ZN6wayobj10makebound2EPiiS0_ : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 473.astar,[.] _ZN7way2obj12releasepointEii : sample=1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 473.astar,[.] _ZN9regwayobj10makebound2ER9flexarrayIP6regobjES4_ : sample=1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 481.wrf,[.] __module_advect_em_MOD_advect_scalar : sample=-2% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 481.wrf,[.] __module_small_step_em_MOD_advance_w : sample=-2% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 481.wrf,libm.so.6 : sample=-1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 481.wrf,libc.so.6 : sample=1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 482.sphinx3,[.] vector_gautbl_eval_logs3 : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_regression output-bmk-results.py(183): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(184): return (result - 100 > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.regression : 482.sphinx3,[.] mgau_eval : sample=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(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 400.perlbench,[.] S_regmatch : sample=-1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 400.perlbench,libc.so.6 : sample=-3% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 401.bzip2,[.] mainSort : sample=-2% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 401.bzip2,[.] mainGtU.part.0 : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 401.bzip2,[.] 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(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 401.bzip2,[.] BZ2_compressBlock : sample=1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 401.bzip2,[.] BZ2_decompress : sample=2% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 403.gcc,libc.so.6 : sample=-5% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 403.gcc,[.] reg_is_remote_constant_p : sample=-2% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 403.gcc,[.] memset : sample=-2% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 410.bwaves,[.] mat_times_vec_ : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 410.bwaves,[.] bi_cgstab_block_ : sample=-3% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 416.gamess,[.] forms_ : sample=1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 416.gamess,[.] twotff_ : sample=-1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 416.gamess,[.] dirfck_ : sample=-1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 416.gamess,[.] xyzint_ : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 429.mcf,[.] primal_bea_mpp : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 429.mcf,[.] refresh_potential : sample=-1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 433.milc,[.] mult_su3_na : sample=1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 433.milc,[.] mult_su3_nn : sample=1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 433.milc,[.] mult_su3_mat_vec : sample=-2% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 433.milc,[.] mult_adj_su3_mat_vec : sample=1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 433.milc,[.] uncompress_anti_hermitian : sample=4% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 434.zeusmp,[.] hsmoc_ : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 434.zeusmp,[.] lorentz_ : sample=-1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 435.gromacs,[.] inl1130_ : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 435.gromacs,[.] search_neighbours : sample=1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 436.cactusADM,[.] bench_staggeredleapfrog2_ : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 437.leslie3d,[.] fluxj_ : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 437.leslie3d,[.] fluxk_ : sample=1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 437.leslie3d,[.] fluxi_ : sample=2% (threshold=15.299999999999999%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 437.leslie3d,[.] extrapi_ : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 437.leslie3d,[.] extrapj_ : sample=-2% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 444.namd,[.] _ZN20ComputeNonbondedUtil26calc_pair_energy_fullelectEP9nonbonded : sample=1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 444.namd,[.] _ZN20ComputeNonbondedUtil19calc_pair_fullelectEP9nonbonded : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 444.namd,[.] _ZN20ComputeNonbondedUtil32calc_pair_energy_merge_fullelectEP9nonbonded : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 444.namd,[.] _ZN20ComputeNonbondedUtil16calc_pair_energyEP9nonbonded : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 444.namd,[.] _ZN20ComputeNonbondedUtil25calc_pair_merge_fullelectEP9nonbonded : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 444.namd,[.] _ZN20ComputeNonbondedUtil9calc_pairEP9nonbonded : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 445.gobmk,[.] do_play_move : sample=-3% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 445.gobmk,[.] fastlib : sample=5% (threshold=15.18%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 447.dealII,[.] _ZNK9MappingQ1ILi3EE12compute_fillERK12TriaIteratorILi3E15DoFCellAccessorILi3EEEjN10QProjectorILi3EE17DataSetDescriptorERNS0_12InternalDataERSt6vectorI5PointILi3EESaISE_EE : sample=1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 447.dealII,[.] _ZNK12SparseMatrixIdE5vmultI6VectorIdES3_EEvRT_RKT0_ : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 447.dealII,libstdc++.so.6.0.33 : sample=-3% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 447.dealII,[.] _ZN13LaplaceSolver6SolverILi3EE22assemble_linear_systemERNS1_12LinearSystemE : sample=-2% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 447.dealII,[.] _ZSt18_Rb_tree_incrementPKSt18_Rb_tree_node_base : sample=-6% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 450.soplex,[.] _ZN6soplex10SPxSteepPR8entered4ENS_5SPxIdEi : sample=2% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 450.soplex,[.] _ZN6soplex8SSVector18assign2productFullERKNS_5SVSetERKS0_ : sample=6% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 450.soplex,[.] _ZN6soplex9CLUFactor16initFactorMatrixEPPNS_7SVectorEd : sample=4% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 453.povray,[.] _ZN3povL23All_Plane_IntersectionsEPNS_13Object_StructEPNS_10Ray_StructEPNS_13istack_structE : sample=-2% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 453.povray,[.] _ZN3povL31All_CSG_Intersect_IntersectionsEPNS_13Object_StructEPNS_10Ray_StructEPNS_13istack_structE : sample=1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 453.povray,[.] _ZN3povL24All_Sphere_IntersectionsEPNS_13Object_StructEPNS_10Ray_StructEPNS_13istack_structE : sample=-9% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 453.povray,[.] _ZN3pov17Check_And_EnqueueEPNS_21Priority_Queue_StructEPNS_16BBox_Tree_StructEPNS_19Bounding_Box_StructEPNS_14Rayinfo_StructE : sample=4% (threshold=15.299999999999999%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 454.calculix,[.] e_c3d_ : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 454.calculix,[.] DVdot33 : sample=1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 456.hmmer,[.] P7Viterbi : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 458.sjeng,[.] std_eval : sample=-2% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 458.sjeng,[.] gen : sample=1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 458.sjeng,[.] setup_attackers : sample=2% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 459.GemsFDTD,[.] __upml_mod_MOD_upmlupdatee : sample=-1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 459.GemsFDTD,[.] __nft_mod_MOD_nft_store : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 459.GemsFDTD,[.] __upml_mod_MOD_upmlupdateh : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 459.GemsFDTD,[.] __update_mod_MOD_updateh_homo : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 459.GemsFDTD,[.] __update_mod_MOD_updatee_homo : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 462.libquantum,[.] quantum_toffoli : sample=-2% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 462.libquantum,[.] quantum_sigma_x : sample=5% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 462.libquantum,[.] quantum_cnot : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 464.h264ref,[.] SetupFastFullPelSearch : sample=-1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 464.h264ref,libc.so.6 : sample=-1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 464.h264ref,[.] __memcpy_neon : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 464.h264ref,[.] FastFullPelBlockMotionSearch : sample=-7% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 465.tonto,libm.so.6 : sample=-2% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 465.tonto,[.] __shell2_module_MOD_make_ft_1 : sample=5% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 465.tonto,[.] __sincosl : sample=2% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 465.tonto,[.] __shell1quartet_module_MOD_make_esfs.isra.0 : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 465.tonto,[.] __cexpl : sample=-5% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 470.lbm,[.] LBM_performStreamCollide : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 471.omnetpp,[.] _ZN12cMessageHeap8getFirstEv : sample=-7% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 471.omnetpp,libc.so.6 : sample=1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 471.omnetpp,[.] _ZN5cGate7deliverEP8cMessaged : sample=6% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 473.astar,[.] _ZN6wayobj10makebound2EPiiS0_ : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 473.astar,[.] _ZN7way2obj12releasepointEii : sample=1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 473.astar,[.] _ZN9regwayobj10makebound2ER9flexarrayIP6regobjES4_ : sample=1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 481.wrf,[.] __module_advect_em_MOD_advect_scalar : sample=-2% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 481.wrf,[.] __module_small_step_em_MOD_advance_w : sample=-2% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 481.wrf,libm.so.6 : sample=-1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(62): return np.nan output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(107): if metric == "num_vect_loops" or metric == "num_sve_loops": output-bmk-results.py(110): return default_threshold[(change_kind,metric,mode)] output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 481.wrf,libc.so.6 : sample=1% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 482.sphinx3,[.] vector_gautbl_eval_logs3 : sample=0% (threshold=15%) output-bmk-results.py(232): if not is_entry_xxx[change_kind](metric, row["rel_" + metric], threshold): --- modulename: output-bmk-results, funcname: is_entry_improvement output-bmk-results.py(192): if metric in metric_utils.higher_regress_metrics: output-bmk-results.py(193): return (100 - result > threshold) output-bmk-results.py(233): continue output-bmk-results.py(224): for index, row in out_df.iterrows(): output-bmk-results.py(226): threshold = get_threshold(sym_type, metric, mode, row["benchmark"], row["symbol"]) --- modulename: output-bmk-results, funcname: get_threshold output-bmk-results.py(98): if metric == "sample": output-bmk-results.py(99): spec_thr = get_specific_thresholds(metric, mode, bmk, symb) --- modulename: output-bmk-results, funcname: get_specific_thresholds output-bmk-results.py(57): if specific_variability is None: output-bmk-results.py(60): var = specific_variability[ (specific_variability['benchmark'] == bmk) & (specific_variability['symbol'].str.strip() == symb)] output-bmk-results.py(61): if var.empty: output-bmk-results.py(63): elif len(var)>1: output-bmk-results.py(68): if var.iloc[0]['sample_variation_average']>0 : output-bmk-results.py(69): threshold = ( var.iloc[0]['sample_variation_average'] ) output-bmk-results.py(70): if mode == "build": output-bmk-results.py(74): threshold *= 3 output-bmk-results.py(81): return threshold output-bmk-results.py(100): if not np.isnan(spec_thr): output-bmk-results.py(104): spec_thr=max(spec_thr, default_threshold[(change_kind,metric,mode)]) output-bmk-results.py(105): return spec_thr output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ output-bmk-results.py(229): .format(sym_type, change_kind, row["benchmark"], row["symbol"], metric, 100-row["rel_" + metric], threshold)) output-bmk-results.py(228): print("DEBUG: checking {0}.{1} : {2},{3} : {4}={5}% (threshold={6}%)"\ DEBUG: checking symbol.improvement : 482.sphinx3,[.] mgau_eval : sample=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(33): os.remove(self.filename) output-bmk-results.py(303): f_ebp.write("\n") --- modulename: output-bmk-results, funcname: write output-bmk-results.py(36): if not self.predicate or not self.outf: 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(33): os.remove(self.filename) output-bmk-results.py(306): f_regr.close() --- modulename: output-bmk-results, funcname: close output-bmk-results.py(29): if not self.outf: output-bmk-results.py(31): self.outf.close() output-bmk-results.py(32): if os.stat(self.filename).st_size == 0: output-bmk-results.py(33): os.remove(self.filename) output-bmk-results.py(307): f_ebp.close() --- modulename: output-bmk-results, funcname: close output-bmk-results.py(29): if not self.outf: 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