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#!/usr/bin/env python2.7
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# Copyright 2017, Google Inc.
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# All rights reserved.
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are
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# met:
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#
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# * Redistributions of source code must retain the above copyright
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# notice, this list of conditions and the following disclaimer.
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# * Redistributions in binary form must reproduce the above
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# copyright notice, this list of conditions and the following disclaimer
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# in the documentation and/or other materials provided with the
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# distribution.
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# * Neither the name of Google Inc. nor the names of its
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# contributors may be used to endorse or promote products derived from
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# this software without specific prior written permission.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
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# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
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# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
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# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
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# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
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# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
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# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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import sys
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import json
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import bm_json
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import tabulate
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import argparse
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import scipy
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def changed_ratio(n, o):
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if float(o) <= .0001: o = 0
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if float(n) <= .0001: n = 0
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if o == 0 and n == 0: return 0
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if o == 0: return 100
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return (float(n)-float(o))/float(o)
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def min_change(pct):
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return lambda n, o: abs(changed_ratio(n,o)) > pct/100.0
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_INTERESTING = [
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'cpu_time',
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'real_time',
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'locks_per_iteration',
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'allocs_per_iteration',
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'writes_per_iteration',
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'atm_cas_per_iteration',
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'atm_add_per_iteration',
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]
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_AVAILABLE_BENCHMARK_TESTS = ['bm_fullstack_unary_ping_pong',
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'bm_fullstack_streaming_ping_pong',
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'bm_fullstack_streaming_pump',
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'bm_closure',
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'bm_cq',
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'bm_call_create',
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'bm_error',
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'bm_chttp2_hpack',
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'bm_chttp2_transport',
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'bm_pollset',
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'bm_metadata',
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'bm_fullstack_trickle']
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argp = argparse.ArgumentParser(description='Perform diff on microbenchmarks')
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argp.add_argument('-t', '--track',
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choices=sorted(_INTERESTING),
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nargs='+',
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default=sorted(_INTERESTING),
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help='Which metrics to track')
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argp.add_argument('-b', '--benchmarks', nargs='+', choices=_AVAILABLE_BENCHMARK_TESTS, default=['bm_error'])
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argp.add_argument('-d', '--diff_base', type=str)
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argp.add_argument('-r', '--repetitions', type=int, default=5)
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argp.add_argument('-p', '--p_threshold', type=float, default=0.05)
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args = argp.parse_args()
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assert args.diff_base
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def collect1(bm, cfg, ver):
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subprocess.check_call(['make', 'clean'])
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subprocess.check_call(
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['make', bm_name,
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'CONFIG=%s' % cfg, '-j', '%d' % multiprocessing.cpu_count()])
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cmd = ['bins/%s/%s' % (cfg, bm),
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'--benchmark_out=%s.%s.%s.json' % (bm, cfg, ver),
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'--benchmark_out_format=json',
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'--benchmark_repetitions=%d' % (args.repetitions)
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]
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subprocess.check_call(cmd)
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for bm in args.benchmarks:
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collect1(bm, 'opt', 'new')
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collect1(bm, 'counters', 'new')
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git_comment = 'Performance differences between this PR and %s\\n' % args.diff_perf
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where_am_i = subprocess.check_output(['git', 'rev-parse', '--abbrev-ref', 'HEAD']).strip()
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subprocess.check_call(['git', 'checkout', args.diff_base])
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try:
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comparables = []
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for bm in args.benchmarks:
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try:
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collect1(bm, 'opt', 'old')
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collect1(bm, 'counters', 'old')
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comparables.append(bm_name)
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except subprocess.CalledProcessError, e:
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pass
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finally:
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subprocess.check_call(['git', 'checkout', where_am_i])
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class Benchmark:
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def __init__(self):
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self.samples = {
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True: collections.defaultdict(list),
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False: collections.defaultdict(list)
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}
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self.final = {}
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def add_sample(self, data, new):
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for f in _INTERESTING:
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if f in data:
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self.samples[new][f].append(data[f])
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def process(self):
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for f in _INTERESTING:
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new = self.samples[True][f]
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old = self.samples[False][f]
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if not new or not old: continue
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p = scipy.stats.ttest_ind(new, old)
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if p < args.p_threshold:
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self.final[f] = avg(new) - avg(old)
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return self.final.keys()
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def row(self, flds):
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return [self.final[f] if f in self.final else '' for f in flds]
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benchmarks = collections.defaultdict(Benchmark)
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for bm in comparables:
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with open('%s.counters.new.json' % bm) as f:
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js_new_ctr = json.loads(f.read())
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with open('%s.opt.new.json' % bm) as f:
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js_new_opt = json.loads(f.read())
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with open('%s.counters.old.json' % bm) as f:
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js_old_ctr = json.loads(f.read())
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with open('%s.opt.old.json' % bm) as f:
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js_old_opt = json.loads(f.read())
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for row in bm_json.expand_json(js_new_ctr, js_new_opt):
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name = row['cpp_name']
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if name.endswith('_mean') or nme.endswith('_stddev'): continue
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benchmarks[name].add_sample(row, True)
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for row in bm_json.expand_json(js_old_ctr, js_old_opt):
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name = row['cpp_name']
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if name.endswith('_mean') or nme.endswith('_stddev'): continue
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benchmarks[name].add_sample(row, False)
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really_interesting = set()
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for bm in benchmarks:
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really_interesting.update(bm.process())
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fields = [f for f in _INTERESTING if f in really_interesting]
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headers = ['Benchmark'] + fields
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rows = []
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for name in sorted(benchmarks.keys()):
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rows.append([name] + benchmarks[name].row(fields))
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print tabulate.tabulate(rows, headers=headers, floatfmt='+.2f')
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