grpc/tools/profiling/microbenchmarks/bm_diff/bm_run.py

#!/usr/bin/env python2.7
# Copyright 2017, Google Inc.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
#     * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
#     * Redistributions in binary form must reproduce the above
# copyright notice, this list of conditions and the following disclaimer
# in the documentation and/or other materials provided with the
# distribution.
#     * Neither the name of Google Inc. nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
""" Python utility to run opt and counters benchmarks and save json output """

import bm_constants

import argparse
import subprocess
import multiprocessing
import random
import itertools
import sys
import os

sys.path.append(
    os.path.join(
        os.path.dirname(sys.argv[0]), '..', '..', '..', 'run_tests',
        'python_utils'))
import jobset


def _args():
    argp = argparse.ArgumentParser(description='Runs microbenchmarks')
    argp.add_argument(
        '-b',
        '--benchmarks',
        nargs='+',
        choices=bm_constants._AVAILABLE_BENCHMARK_TESTS,
        default=bm_constants._AVAILABLE_BENCHMARK_TESTS,
        help='Benchmarks to run')
    argp.add_argument(
        '-j',
        '--jobs',
        type=int,
        default=multiprocessing.cpu_count(),
        help='Number of CPUs to use')
    argp.add_argument(
        '-n',
        '--name',
        type=str,
        help='Unique name of the build to run. Needs to match the handle passed to bm_build.py'
    )
    argp.add_argument(
        '-r',
        '--repetitions',
        type=int,
        default=1,
        help='Number of repetitions to pass to the benchmarks')
    argp.add_argument(
        '-l',
        '--loops',
        type=int,
        default=20,
        help='Number of times to loops the benchmarks. More loops cuts down on noise'
    )
    args = argp.parse_args()
    assert args.name
    if args.loops < 3:
        print "WARNING: This run will likely be noisy. Increase loops to at least 3."
    return args


def _collect_bm_data(bm, cfg, name, reps, idx, loops):
    jobs_list = []
    for line in subprocess.check_output(
        ['bm_diff_%s/%s/%s' % (name, cfg, bm),
         '--benchmark_list_tests']).splitlines():
        stripped_line = line.strip().replace("/", "_").replace(
            "<", "_").replace(">", "_").replace(", ", "_")
        cmd = [
            'bm_diff_%s/%s/%s' % (name, cfg, bm), '--benchmark_filter=^%s$' %
            line, '--benchmark_out=%s.%s.%s.%s.%d.json' %
            (bm, stripped_line, cfg, name, idx), '--benchmark_out_format=json',
            '--benchmark_repetitions=%d' % (reps)
        ]
        jobs_list.append(
            jobset.JobSpec(
                cmd,
                shortname='%s %s %s %s %d/%d' % (bm, line, cfg, name, idx + 1,
                                                 loops),
                verbose_success=True,
                timeout_seconds=60 * 2))
    return jobs_list


def run(name, benchmarks, jobs, loops, reps):
    jobs_list = []
    for loop in range(0, loops):
        for bm in benchmarks:
            jobs_list += _collect_bm_data(bm, 'opt', name, reps, loop, loops)
            jobs_list += _collect_bm_data(bm, 'counters', name, reps, loop,
                                          loops)
    random.shuffle(jobs_list, random.SystemRandom().random)
    jobset.run(jobs_list, maxjobs=jobs)


if __name__ == '__main__':
    args = _args()
    run(args.name, args.benchmarks, args.jobs, args.loops, args.repetitions)