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