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1#!/usr/bin/python2
2#
3# Copyright 2015 The ANGLE Project Authors. All rights reserved.
4# Use of this source code is governed by a BSD-style license that can be
5# found in the LICENSE file.
6#
7# perf_test_runner.py:
8#   Helper script for running and analyzing perftest results. Runs the
9#   tests in an infinite batch, printing out the mean and coefficient of
10#   variation of the population continuously.
11#
12
13import glob
14import subprocess
15import sys
16import os
17import re
18
19base_path = os.path.abspath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..'))
20
21# Look for a [Rr]elease build.
22perftests_paths = glob.glob('out/*elease*')
23metric = 'wall_time'
24max_experiments = 10
25
26binary_name = 'angle_perftests'
27if sys.platform == 'win32':
28    binary_name += '.exe'
29
30scores = []
31
32
33# Danke to http://stackoverflow.com/a/27758326
34def mean(data):
35    """Return the sample arithmetic mean of data."""
36    n = len(data)
37    if n < 1:
38        raise ValueError('mean requires at least one data point')
39    return float(sum(data)) / float(n)  # in Python 2 use sum(data)/float(n)
40
41
42def sum_of_square_deviations(data, c):
43    """Return sum of square deviations of sequence data."""
44    ss = sum((float(x) - c)**2 for x in data)
45    return ss
46
47
48def coefficient_of_variation(data):
49    """Calculates the population coefficient of variation."""
50    n = len(data)
51    if n < 2:
52        raise ValueError('variance requires at least two data points')
53    c = mean(data)
54    ss = sum_of_square_deviations(data, c)
55    pvar = ss / n  # the population variance
56    stddev = (pvar**0.5)  # population standard deviation
57    return stddev / c
58
59
60def truncated_list(data, n):
61    """Compute a truncated list, n is truncation size"""
62    if len(data) < n * 2:
63        raise ValueError('list not large enough to truncate')
64    return sorted(data)[n:-n]
65
66
67def truncated_mean(data, n):
68    """Compute a truncated mean, n is truncation size"""
69    return mean(truncated_list(data, n))
70
71
72def truncated_cov(data, n):
73    """Compute a truncated coefficient of variation, n is truncation size"""
74    return coefficient_of_variation(truncated_list(data, n))
75
76
77# Find most recent binary
78newest_binary = None
79newest_mtime = None
80
81for path in perftests_paths:
82    binary_path = os.path.join(base_path, path, binary_name)
83    if os.path.exists(binary_path):
84        binary_mtime = os.path.getmtime(binary_path)
85        if (newest_binary is None) or (binary_mtime > newest_mtime):
86            newest_binary = binary_path
87            newest_mtime = binary_mtime
88
89perftests_path = newest_binary
90
91if perftests_path == None or not os.path.exists(perftests_path):
92    print('Cannot find Release %s!' % binary_name)
93    sys.exit(1)
94
95if sys.platform == 'win32':
96    test_name = 'DrawCallPerfBenchmark.Run/d3d11_null'
97else:
98    test_name = 'DrawCallPerfBenchmark.Run/gl'
99
100if len(sys.argv) >= 2:
101    test_name = sys.argv[1]
102
103print('Using test executable: ' + perftests_path)
104print('Test name: ' + test_name)
105
106
107def get_results(metric, extra_args=[]):
108    process = subprocess.Popen(
109        [perftests_path, '--gtest_filter=' + test_name] + extra_args,
110        stdout=subprocess.PIPE,
111        stderr=subprocess.PIPE)
112    output, err = process.communicate()
113
114    m = re.search(r'Running (\d+) tests', output)
115    if m and int(m.group(1)) > 1:
116        print("Found more than one test result in output:")
117        print(output)
118        sys.exit(3)
119
120    # Results are reported in the format:
121    # name_backend.metric: story= value units.
122    pattern = r'\.' + metric + r':.*= ([0-9.]+)'
123    m = re.findall(pattern, output)
124    if not m:
125        print("Did not find the metric '%s' in the test output:" % metric)
126        print(output)
127        sys.exit(1)
128
129    return [float(value) for value in m]
130
131
132# Calibrate the number of steps
133steps = get_results("steps", ["--calibration"])[0]
134print("running with %d steps." % steps)
135
136# Loop 'max_experiments' times, running the tests.
137for experiment in range(max_experiments):
138    experiment_scores = get_results(metric, ["--steps", str(steps)])
139
140    for score in experiment_scores:
141        sys.stdout.write("%s: %.2f" % (metric, score))
142        scores.append(score)
143
144        if (len(scores) > 1):
145            sys.stdout.write(", mean: %.2f" % mean(scores))
146            sys.stdout.write(", variation: %.2f%%" % (coefficient_of_variation(scores) * 100.0))
147
148        if (len(scores) > 7):
149            truncation_n = len(scores) >> 3
150            sys.stdout.write(", truncated mean: %.2f" % truncated_mean(scores, truncation_n))
151            sys.stdout.write(", variation: %.2f%%" % (truncated_cov(scores, truncation_n) * 100.0))
152
153        print("")
154