150 lines
4.5 KiB
Python
Executable File
150 lines
4.5 KiB
Python
Executable File
#!/usr/bin/python2
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#
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# Copyright 2019 The Dawn Authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Based on Angle's perf_test_runner.py
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import glob
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import subprocess
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import sys
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import os
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import re
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base_path = os.path.abspath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..'))
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# Look for a [Rr]elease build.
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perftests_paths = glob.glob('out/*elease*')
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metric = 'wall_time'
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max_experiments = 10
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binary_name = 'dawn_perf_tests'
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if sys.platform == 'win32':
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binary_name += '.exe'
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scores = []
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def mean(data):
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"""Return the sample arithmetic mean of data."""
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n = len(data)
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if n < 1:
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raise ValueError('mean requires at least one data point')
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return float(sum(data)) / float(n) # in Python 2 use sum(data)/float(n)
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def sum_of_square_deviations(data, c):
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"""Return sum of square deviations of sequence data."""
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ss = sum((float(x) - c)**2 for x in data)
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return ss
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def coefficient_of_variation(data):
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"""Calculates the population coefficient of variation."""
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n = len(data)
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if n < 2:
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raise ValueError('variance requires at least two data points')
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c = mean(data)
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ss = sum_of_square_deviations(data, c)
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pvar = ss / n # the population variance
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stddev = (pvar**0.5) # population standard deviation
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return stddev / c
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def truncated_list(data, n):
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"""Compute a truncated list, n is truncation size"""
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if len(data) < n * 2:
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raise ValueError('list not large enough to truncate')
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return sorted(data)[n:-n]
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def truncated_mean(data, n):
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"""Compute a truncated mean, n is truncation size"""
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return mean(truncated_list(data, n))
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def truncated_cov(data, n):
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"""Compute a truncated coefficient of variation, n is truncation size"""
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return coefficient_of_variation(truncated_list(data, n))
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# Find most recent binary
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newest_binary = None
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newest_mtime = None
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for path in perftests_paths:
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binary_path = os.path.join(base_path, path, binary_name)
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if os.path.exists(binary_path):
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binary_mtime = os.path.getmtime(binary_path)
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if (newest_binary is None) or (binary_mtime > newest_mtime):
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newest_binary = binary_path
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newest_mtime = binary_mtime
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perftests_path = newest_binary
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if perftests_path == None or not os.path.exists(perftests_path):
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print('Cannot find Release %s!' % binary_name)
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sys.exit(1)
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if len(sys.argv) >= 2:
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test_name = sys.argv[1]
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print('Using test executable: ' + perftests_path)
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print('Test name: ' + test_name)
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def get_results(metric, extra_args=[]):
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process = subprocess.Popen(
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[perftests_path, '--gtest_filter=' + test_name] + extra_args,
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stdout=subprocess.PIPE,
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stderr=subprocess.PIPE)
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output, err = process.communicate()
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m = re.search(r'Running (\d+) tests', output)
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if m and int(m.group(1)) > 1:
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print("Found more than one test result in output:")
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print(output)
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sys.exit(3)
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pattern = metric + r'.*= ([0-9.]+)'
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m = re.findall(pattern, output)
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if not m:
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print("Did not find the metric '%s' in the test output:" % metric)
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print(output)
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sys.exit(1)
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return [float(value) for value in m]
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# Calibrate the number of steps
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steps = get_results("steps", ["--calibration"])[0]
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print("running with %d steps." % steps)
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# Loop 'max_experiments' times, running the tests.
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for experiment in range(max_experiments):
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experiment_scores = get_results(metric, ["--override-steps", str(steps)])
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for score in experiment_scores:
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sys.stdout.write("%s: %.2f" % (metric, score))
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scores.append(score)
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if (len(scores) > 1):
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sys.stdout.write(", mean: %.2f" % mean(scores))
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sys.stdout.write(", variation: %.2f%%" % (coefficient_of_variation(scores) * 100.0))
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if (len(scores) > 7):
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truncation_n = len(scores) >> 3
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sys.stdout.write(", truncated mean: %.2f" % truncated_mean(scores, truncation_n))
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sys.stdout.write(", variation: %.2f%%" % (truncated_cov(scores, truncation_n) * 100.0))
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print("")
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