blob: 157d4497a14e6d2238bd3ad4bcaf839d271ffb15 [file] [log] [blame]
#!/usr/bin/env python3
# Copyright 2019 The Dawn Authors
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
# Based on Angle's
import glob
import subprocess
import sys
import os
import re
base_path = os.path.abspath(
os.path.join(os.path.dirname(os.path.abspath(__file__)), '..'))
# Look for a [Rr]elease build.
perftests_paths = glob.glob('out/*elease*')
metric = 'wall_time'
max_experiments = 10
binary_name = 'dawn_perf_tests'
if sys.platform == 'win32':
binary_name += '.exe'
scores = []
def mean(data):
"""Return the sample arithmetic mean of data."""
n = len(data)
if n < 1:
raise ValueError('mean requires at least one data point')
return float(sum(data)) / float(n) # in Python 2 use sum(data)/float(n)
def sum_of_square_deviations(data, c):
"""Return sum of square deviations of sequence data."""
ss = sum((float(x) - c)**2 for x in data)
return ss
def coefficient_of_variation(data):
"""Calculates the population coefficient of variation."""
n = len(data)
if n < 2:
raise ValueError('variance requires at least two data points')
c = mean(data)
ss = sum_of_square_deviations(data, c)
pvar = ss / n # the population variance
stddev = (pvar**0.5) # population standard deviation
return stddev / c
def truncated_list(data, n):
"""Compute a truncated list, n is truncation size"""
if len(data) < n * 2:
raise ValueError('list not large enough to truncate')
return sorted(data)[n:-n]
def truncated_mean(data, n):
"""Compute a truncated mean, n is truncation size"""
return mean(truncated_list(data, n))
def truncated_cov(data, n):
"""Compute a truncated coefficient of variation, n is truncation size"""
return coefficient_of_variation(truncated_list(data, n))
# Find most recent binary
newest_binary = None
newest_mtime = None
for path in perftests_paths:
binary_path = os.path.join(base_path, path, binary_name)
if os.path.exists(binary_path):
binary_mtime = os.path.getmtime(binary_path)
if (newest_binary is None) or (binary_mtime > newest_mtime):
newest_binary = binary_path
newest_mtime = binary_mtime
perftests_path = newest_binary
if perftests_path == None or not os.path.exists(perftests_path):
print('Cannot find Release %s!' % binary_name)
if len(sys.argv) >= 2:
test_name = sys.argv[1]
print('Using test executable: ' + perftests_path)
print('Test name: ' + test_name)
def get_results(metric, extra_args=[]):
process = subprocess.Popen(
[perftests_path, '--gtest_filter=' + test_name] + extra_args,
output, err = process.communicate()
m ='Running (\d+) tests', output)
if m and int( > 1:
print("Found more than one test result in output:")
pattern = metric + r'.*= ([0-9.]+)'
m = re.findall(pattern, output)
if not m:
print("Did not find the metric '%s' in the test output:" % metric)
return [float(value) for value in m]
# Calibrate the number of steps
steps = get_results("steps", ["--calibration"])[0]
print("running with %d steps." % steps)
# Loop 'max_experiments' times, running the tests.
for experiment in range(max_experiments):
experiment_scores = get_results(metric, ["--override-steps", str(steps)])
for score in experiment_scores:
sys.stdout.write("%s: %.2f" % (metric, score))
if (len(scores) > 1):
sys.stdout.write(", mean: %.2f" % mean(scores))
sys.stdout.write(", variation: %.2f%%" %
(coefficient_of_variation(scores) * 100.0))
if (len(scores) > 7):
truncation_n = len(scores) >> 3
sys.stdout.write(", truncated mean: %.2f" %
truncated_mean(scores, truncation_n))
sys.stdout.write(", variation: %.2f%%" %
(truncated_cov(scores, truncation_n) * 100.0))