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|
--- modulename: output-bmk-results, funcname: <module>
<string>(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: <genexpr>
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: <genexpr>
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: <genexpr>
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: <genexpr>
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: <genexpr>
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: <genexpr>
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: <genexpr>
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)
Empty DataFrame
Columns: [benchmark, symbol, rel_sample, rel_size, rel_num_vect_loops, rel_num_sve_loops, rel_symbol_md5sum, rel_status, sample_x, sample_y, size_x, size_y, num_vect_loops_x, num_vect_loops_y, num_sve_loops_x, num_sve_loops_y, symbol_md5sum_x, symbol_md5sum_y, status_x, status_y]
Index: []
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)
Empty DataFrame
Columns: [benchmark, symbol, rel_sample, rel_size, rel_num_vect_loops, rel_num_sve_loops, rel_symbol_md5sum, rel_status, sample_x, sample_y, size_x, size_y, num_vect_loops_x, num_vect_loops_y, num_sve_loops_x, num_sve_loops_y, symbol_md5sum_x, symbol_md5sum_y, status_x, status_y]
Index: []
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(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(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(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 : coremark,coremark : size=0% (threshold=10%)
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(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 : coremark,coremark : size=0% (threshold=10%)
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
|
