deae7e982a
The benchmarking infrastructure can now run in two modes: - Sweep Mode: which generates a ramp of size values (same as before), - Distribution Mode: allows the user to select a distribution for the size paramater that is representative from production. The analysis tool has also been updated to handle both modes. Differential Revision: https://reviews.llvm.org/D93210
129 lines
4.7 KiB
Python
129 lines
4.7 KiB
Python
"""Reads JSON files produced by the benchmarking framework and renders them.
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Installation:
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> apt-get install python3-pip
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> pip3 install matplotlib pandas seaborn
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Run:
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> python3 libc/benchmarks/libc-benchmark-analysis.py3 <files>
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"""
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import argparse
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import json
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import pandas as pd
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import seaborn as sns
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import matplotlib.pyplot as plt
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from matplotlib.ticker import EngFormatter
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def formatUnit(value, unit):
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return EngFormatter(unit, sep="").format_data(value)
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def formatCache(cache):
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letter = cache["Type"][0].lower()
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level = cache["Level"]
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size = formatUnit(cache["Size"], "B")
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ways = cache["NumSharing"]
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return F'{letter}L{level}:{size}/{ways}'
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def getCpuFrequency(study):
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return study["Runtime"]["Host"]["CpuFrequency"]
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def getId(study):
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CpuName = study["Runtime"]["Host"]["CpuName"]
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CpuFrequency = formatUnit(getCpuFrequency(study), "Hz")
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Mode = " (Sweep)" if study["Configuration"]["IsSweepMode"] else ""
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CpuCaches = ", ".join(formatCache(c) for c in study["Runtime"]["Host"]["Caches"])
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return F'{CpuName} {CpuFrequency}{Mode}\n{CpuCaches}'
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def getFunction(study):
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return study["Configuration"]["Function"]
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def getLabel(study):
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return F'{getFunction(study)} {study["StudyName"]}'
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def displaySweepData(id, studies, mode):
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df = None
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for study in studies:
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Measurements = study["Measurements"]
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SweepModeMaxSize = study["Configuration"]["SweepModeMaxSize"]
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NumSizes = SweepModeMaxSize + 1
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NumTrials = study["Configuration"]["NumTrials"]
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assert NumTrials * NumSizes == len(Measurements), 'not a multiple of NumSizes'
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Index = pd.MultiIndex.from_product([range(NumSizes), range(NumTrials)], names=['size', 'trial'])
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if df is None:
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df = pd.DataFrame(Measurements, index=Index, columns=[getLabel(study)])
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else:
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df[getLabel(study)] = pd.Series(Measurements, index=Index)
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df = df.reset_index(level='trial', drop=True)
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if mode == "cycles":
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df *= getCpuFrequency(study)
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if mode == "bytespercycle":
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df *= getCpuFrequency(study)
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for col in df.columns:
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df[col] = pd.Series(data=df.index, index=df.index).divide(df[col])
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FormatterUnit = {"time":"s","cycles":"","bytespercycle":"B/cycle"}[mode]
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Label = {"time":"Time","cycles":"Cycles","bytespercycle":"Byte/cycle"}[mode]
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graph = sns.lineplot(data=df, palette="muted", ci=95)
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graph.set_title(id)
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graph.yaxis.set_major_formatter(EngFormatter(unit=FormatterUnit))
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graph.yaxis.set_label_text(Label)
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graph.xaxis.set_major_formatter(EngFormatter(unit="B"))
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graph.xaxis.set_label_text("Copy Size")
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_ = plt.xticks(rotation=90)
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plt.show()
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def displayDistributionData(id, studies, mode):
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distributions = set()
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df = None
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for study in studies:
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distribution = study["Configuration"]["SizeDistributionName"]
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distributions.add(distribution)
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local = pd.DataFrame(study["Measurements"], columns=["time"])
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local["distribution"] = distribution
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local["label"] = getLabel(study)
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local["cycles"] = local["time"] * getCpuFrequency(study)
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if df is None:
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df = local
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else:
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df = df.append(local)
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if mode == "bytespercycle":
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mode = "time"
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print("`--mode=bytespercycle` is ignored for distribution mode reports")
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FormatterUnit = {"time":"s","cycles":""}[mode]
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Label = {"time":"Time","cycles":"Cycles"}[mode]
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graph = sns.violinplot(data=df, x="distribution", y=mode, palette="muted", hue="label", order=sorted(distributions))
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graph.set_title(id)
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graph.yaxis.set_major_formatter(EngFormatter(unit=FormatterUnit))
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graph.yaxis.set_label_text(Label)
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_ = plt.xticks(rotation=90)
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plt.show()
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def main():
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parser = argparse.ArgumentParser(description="Process benchmark json files.")
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parser.add_argument("--mode", choices=["time", "cycles", "bytespercycle"], default="time", help="Use to display either 'time', 'cycles' or 'bytes/cycle'.")
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parser.add_argument("files", nargs="+", help="The json files to read from.")
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args = parser.parse_args()
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study_groups = dict()
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for file in args.files:
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with open(file) as json_file:
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json_obj = json.load(json_file)
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Id = getId(json_obj)
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if Id in study_groups:
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study_groups[Id].append(json_obj)
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else:
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study_groups[Id] = [json_obj]
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plt.tight_layout()
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sns.set_theme(style="ticks")
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for id, study_collection in study_groups.items():
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if "(Sweep)" in id:
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displaySweepData(id, study_collection, args.mode)
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else:
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displayDistributionData(id, study_collection, args.mode)
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if __name__ == "__main__":
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main()
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