7ba39cb9a6
These are the scripts I've been homegrowing for measuring perf impact. I think we found them useful today as a way of sifting through the noise. BUG=skia: Review URL: https://codereview.chromium.org/703713002
40 lines
1.2 KiB
Python
Executable File
40 lines
1.2 KiB
Python
Executable File
#!/usr/bin/env python
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import sys
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from scipy.stats import mannwhitneyu
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SIGNIFICANCE_THRESHOLD = 0.0001
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a,b = {},{}
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for (path, d) in [(sys.argv[1], a), (sys.argv[2], b)]:
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for line in open(path):
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try:
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tokens = line.split()
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samples = tokens[:-1]
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label = tokens[-1]
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d[label] = map(float, samples)
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except:
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pass
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common = set(a.keys()).intersection(b.keys())
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ps = []
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for key in common:
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_, p = mannwhitneyu(a[key], b[key]) # Non-parametric t-test. Doesn't assume normal dist.
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am, bm = min(a[key]), min(b[key])
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ps.append((bm/am, p, key, am, bm))
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ps.sort(reverse=True)
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def humanize(ns):
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for threshold, suffix in [(1e9, 's'), (1e6, 'ms'), (1e3, 'us'), (1e0, 'ns')]:
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if ns > threshold:
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return "%.3g%s" % (ns/threshold, suffix)
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maxlen = max(map(len, common))
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# We print only signficant changes in benchmark timing distribution.
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bonferroni = SIGNIFICANCE_THRESHOLD / len(ps) # Adjust for the fact we've run multiple tests.
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for ratio, p, key, am, bm in ps:
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if p < bonferroni:
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print '%*s\t%6s -> %6s\t%.2gx' % (maxlen, key, humanize(am), humanize(bm), ratio)
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