bm_run.py

#!/usr/bin/env python2.7
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### Python utility to run opt and counters benchmarks and save json output """

import bm_constants

import argparse
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."
    return args


def _collect_bm_data(bm, cfg, name, reps, idx, loops):
    cmd = [
        'bm_diff_%s/%s/%s' % (name, cfg, bm),
        '--benchmark_out=%s.%s.%s.%d.json' % (bm, cfg, name, idx),
        '--benchmark_out_format=json', '--benchmark_repetitions=%d' % (reps)
    ]
    return jobset.JobSpec(
        cmd,
        shortname='%s %s %s %d/%d' % (bm, cfg, name, idx + 1, loops),
        verbose_success=True,
        timeout_seconds=None)


def run(name, benchmarks, jobs, loops, reps):
    jobs_list = []
    for loop in range(0, loops):
        jobs_list.extend(
            x
            for x in itertools.chain(
                (_collect_bm_data(bm, 'opt', name, reps, loop, loops)
                 for bm in benchmarks),
                (_collect_bm_data(bm, 'counters', name, reps, loop, loops)
                 for bm in benchmarks),))
    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)