#!/usr/bin/env python import argparse import sys have_scipy = True try: import scipy.stats except: have_scipy = False SIGNIFICANCE_THRESHOLD = 0.0001 parser = argparse.ArgumentParser( formatter_class=argparse.RawDescriptionHelpFormatter, description='Compare performance of two runs from nanobench.') parser.add_argument('--use_means', action='store_true', default=False, help='Use means to calculate performance ratios.') parser.add_argument('baseline', help='Baseline file.') parser.add_argument('experiment', help='Experiment file.') args = parser.parse_args() a,b = {},{} for (path, d) in [(args.baseline, a), (args.experiment, b)]: for line in open(path): try: tokens = line.split() if tokens[0] != "Samples:": continue samples = tokens[1:-1] label = tokens[-1] d[label] = map(float, samples) except: pass common = set(a.keys()).intersection(b.keys()) def mean(xs): return sum(xs) / len(xs) ps = [] for key in common: p, asem, bsem = 0, 0, 0 m = mean if args.use_means else min am, bm = m(a[key]), m(b[key]) if have_scipy: _, p = scipy.stats.mannwhitneyu(a[key], b[key]) asem, bsem = scipy.stats.sem(a[key]), scipy.stats.sem(b[key]) ps.append((bm/am, p, key, am, bm, asem, bsem)) ps.sort(reverse=True) def humanize(ns): for threshold, suffix in [(1e9, 's'), (1e6, 'ms'), (1e3, 'us'), (1e0, 'ns')]: if ns > threshold: return "%.3g%s" % (ns/threshold, suffix) maxlen = max(map(len, common)) # We print only signficant changes in benchmark timing distribution. bonferroni = SIGNIFICANCE_THRESHOLD / len(ps) # Adjust for the fact we've run multiple tests. for ratio, p, key, am, bm, asem, bsem in ps: if p < bonferroni: str_ratio = ('%.2gx' if ratio < 1 else '%.3gx') % ratio if args.use_means: print '%*s\t%6s(%6s) -> %6s(%6s)\t%s' % (maxlen, key, humanize(am), humanize(asem), humanize(bm), humanize(bsem), str_ratio) else: print '%*s\t%6s -> %6s\t%s' % (maxlen, key, humanize(am), humanize(bm), str_ratio)