forked from OSchip/llvm-project
159 lines
5.1 KiB
Python
159 lines
5.1 KiB
Python
"""
|
|
Static Analyzer qualification infrastructure.
|
|
|
|
This source file contains all the functionality related to benchmarking
|
|
the analyzer on a set projects. Right now, this includes measuring
|
|
execution time and peak memory usage. Benchmark runs analysis on every
|
|
project multiple times to get a better picture about the distribution
|
|
of measured values.
|
|
|
|
Additionally, this file includes a comparison routine for two benchmarking
|
|
results that plots the result together on one chart.
|
|
"""
|
|
|
|
import SATestUtils as utils
|
|
from SATestBuild import ProjectTester, stdout, TestInfo
|
|
from ProjectMap import ProjectInfo
|
|
|
|
import pandas as pd
|
|
from typing import List, Tuple
|
|
|
|
|
|
INDEX_COLUMN = "index"
|
|
|
|
|
|
def _save(data: pd.DataFrame, file_path: str):
|
|
data.to_csv(file_path, index_label=INDEX_COLUMN)
|
|
|
|
|
|
def _load(file_path: str) -> pd.DataFrame:
|
|
return pd.read_csv(file_path, index_col=INDEX_COLUMN)
|
|
|
|
|
|
class Benchmark:
|
|
"""
|
|
Becnhmark class encapsulates one functionality: it runs the analysis
|
|
multiple times for the given set of projects and stores results in the
|
|
specified file.
|
|
"""
|
|
def __init__(self, projects: List[ProjectInfo], iterations: int,
|
|
output_path: str):
|
|
self.projects = projects
|
|
self.iterations = iterations
|
|
self.out = output_path
|
|
|
|
def run(self):
|
|
results = [self._benchmark_project(project)
|
|
for project in self.projects]
|
|
|
|
data = pd.concat(results, ignore_index=True)
|
|
_save(data, self.out)
|
|
|
|
def _benchmark_project(self, project: ProjectInfo) -> pd.DataFrame:
|
|
if not project.enabled:
|
|
stdout(f" \n\n--- Skipping disabled project {project.name}\n")
|
|
return
|
|
|
|
stdout(f" \n\n--- Benchmarking project {project.name}\n")
|
|
|
|
test_info = TestInfo(project)
|
|
tester = ProjectTester(test_info, silent=True)
|
|
project_dir = tester.get_project_dir()
|
|
output_dir = tester.get_output_dir()
|
|
|
|
raw_data = []
|
|
|
|
for i in range(self.iterations):
|
|
stdout(f"Iteration #{i + 1}")
|
|
time, mem = tester.build(project_dir, output_dir)
|
|
raw_data.append({"time": time, "memory": mem,
|
|
"iteration": i, "project": project.name})
|
|
stdout(f"time: {utils.time_to_str(time)}, "
|
|
f"peak memory: {utils.memory_to_str(mem)}")
|
|
|
|
return pd.DataFrame(raw_data)
|
|
|
|
|
|
def compare(old_path: str, new_path: str, plot_file: str):
|
|
"""
|
|
Compare two benchmarking results stored as .csv files
|
|
and produce a plot in the specified file.
|
|
"""
|
|
old = _load(old_path)
|
|
new = _load(new_path)
|
|
|
|
old_projects = set(old["project"])
|
|
new_projects = set(new["project"])
|
|
common_projects = old_projects & new_projects
|
|
|
|
# Leave only rows for projects common to both dataframes.
|
|
old = old[old["project"].isin(common_projects)]
|
|
new = new[new["project"].isin(common_projects)]
|
|
|
|
old, new = _normalize(old, new)
|
|
|
|
# Seaborn prefers all the data to be in one dataframe.
|
|
old["kind"] = "old"
|
|
new["kind"] = "new"
|
|
data = pd.concat([old, new], ignore_index=True)
|
|
|
|
# TODO: compare data in old and new dataframes using statistical tests
|
|
# to check if they belong to the same distribution
|
|
_plot(data, plot_file)
|
|
|
|
|
|
def _normalize(old: pd.DataFrame,
|
|
new: pd.DataFrame) -> Tuple[pd.DataFrame, pd.DataFrame]:
|
|
# This creates a dataframe with all numerical data averaged.
|
|
means = old.groupby("project").mean()
|
|
return _normalize_impl(old, means), _normalize_impl(new, means)
|
|
|
|
|
|
def _normalize_impl(data: pd.DataFrame, means: pd.DataFrame):
|
|
# Right now 'means' has one row corresponding to one project,
|
|
# while 'data' has N rows for each project (one for each iteration).
|
|
#
|
|
# In order for us to work easier with this data, we duplicate
|
|
# 'means' data to match the size of the 'data' dataframe.
|
|
#
|
|
# All the columns from 'data' will maintain their names, while
|
|
# new columns coming from 'means' will have "_mean" suffix.
|
|
joined_data = data.merge(means, on="project", suffixes=("", "_mean"))
|
|
_normalize_key(joined_data, "time")
|
|
_normalize_key(joined_data, "memory")
|
|
return joined_data
|
|
|
|
|
|
def _normalize_key(data: pd.DataFrame, key: str):
|
|
norm_key = _normalized_name(key)
|
|
mean_key = f"{key}_mean"
|
|
data[norm_key] = data[key] / data[mean_key]
|
|
|
|
|
|
def _normalized_name(name: str) -> str:
|
|
return f"normalized {name}"
|
|
|
|
|
|
def _plot(data: pd.DataFrame, plot_file: str):
|
|
import matplotlib
|
|
import seaborn as sns
|
|
from matplotlib import pyplot as plt
|
|
|
|
sns.set_style("whitegrid")
|
|
# We want to have time and memory charts one above the other.
|
|
figure, (ax1, ax2) = plt.subplots(2, 1, figsize=(8, 6))
|
|
|
|
def _subplot(key: str, ax: matplotlib.axes.Axes):
|
|
sns.boxplot(x="project", y=_normalized_name(key), hue="kind",
|
|
data=data, palette=sns.color_palette("BrBG", 2), ax=ax)
|
|
|
|
_subplot("time", ax1)
|
|
# No need to have xlabels on both top and bottom charts.
|
|
ax1.set_xlabel("")
|
|
|
|
_subplot("memory", ax2)
|
|
# The legend on the top chart is enough.
|
|
ax2.get_legend().remove()
|
|
|
|
figure.savefig(plot_file)
|