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run_microbenchmark.py
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run_microbenchmark.py
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#!/usr/bin/env python
# Copyright 2017 gRPC authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import cgi
import multiprocessing
import os
import subprocess
import sys
import argparse
import python_utils.jobset as jobset
import python_utils.start_port_server as start_port_server
sys.path.append(os.path.join(os.path.dirname(sys.argv[0]), '..', 'profiling', 'microbenchmarks', 'bm_diff'))
import bm_constants
flamegraph_dir = os.path.join(os.path.expanduser('~'), 'FlameGraph')
os.chdir(os.path.join(os.path.dirname(sys.argv[0]), '../..'))
if not os.path.exists('reports'):
os.makedirs('reports')
start_port_server.start_port_server()
def fnize(s):
out = ''
for c in s:
if c in '<>, /':
if len(out) and out[-1] == '_': continue
out += '_'
else:
out += c
return out
# index html
index_html = """
<html>
<head>
<title>Microbenchmark Results</title>
</head>
<body>
"""
def heading(name):
global index_html
index_html += "<h1>%s</h1>\n" % name
def link(txt, tgt):
global index_html
index_html += "<p><a href=\"%s\">%s</a></p>\n" % (
cgi.escape(tgt, quote=True), cgi.escape(txt))
def text(txt):
global index_html
index_html += "<p><pre>%s</pre></p>\n" % cgi.escape(txt)
def collect_latency(bm_name, args):
"""generate latency profiles"""
benchmarks = []
profile_analysis = []
cleanup = []
heading('Latency Profiles: %s' % bm_name)
subprocess.check_call(
['make', bm_name,
'CONFIG=basicprof', '-j', '%d' % multiprocessing.cpu_count()])
for line in subprocess.check_output(['bins/basicprof/%s' % bm_name,
'--benchmark_list_tests']).splitlines():
link(line, '%s.txt' % fnize(line))
benchmarks.append(
jobset.JobSpec(['bins/basicprof/%s' % bm_name,
'--benchmark_filter=^%s$' % line,
'--benchmark_min_time=0.05'],
environ={'LATENCY_TRACE': '%s.trace' % fnize(line)},
shortname='profile-%s' % fnize(line)))
profile_analysis.append(
jobset.JobSpec([sys.executable,
'tools/profiling/latency_profile/profile_analyzer.py',
'--source', '%s.trace' % fnize(line), '--fmt', 'simple',
'--out', 'reports/%s.txt' % fnize(line)], timeout_seconds=20*60,
shortname='analyze-%s' % fnize(line)))
cleanup.append(jobset.JobSpec(['rm', '%s.trace' % fnize(line)]))
# periodically flush out the list of jobs: profile_analysis jobs at least
# consume upwards of five gigabytes of ram in some cases, and so analysing
# hundreds of them at once is impractical -- but we want at least some
# concurrency or the work takes too long
if len(benchmarks) >= min(16, multiprocessing.cpu_count()):
# run up to half the cpu count: each benchmark can use up to two cores
# (one for the microbenchmark, one for the data flush)
jobset.run(benchmarks, maxjobs=max(1, multiprocessing.cpu_count()/2))
jobset.run(profile_analysis, maxjobs=multiprocessing.cpu_count())
jobset.run(cleanup, maxjobs=multiprocessing.cpu_count())
benchmarks = []
profile_analysis = []
cleanup = []
# run the remaining benchmarks that weren't flushed
if len(benchmarks):
jobset.run(benchmarks, maxjobs=max(1, multiprocessing.cpu_count()/2))
jobset.run(profile_analysis, maxjobs=multiprocessing.cpu_count())
jobset.run(cleanup, maxjobs=multiprocessing.cpu_count())
def collect_perf(bm_name, args):
"""generate flamegraphs"""
heading('Flamegraphs: %s' % bm_name)
subprocess.check_call(
['make', bm_name,
'CONFIG=mutrace', '-j', '%d' % multiprocessing.cpu_count()])
benchmarks = []
profile_analysis = []
cleanup = []
for line in subprocess.check_output(['bins/mutrace/%s' % bm_name,
'--benchmark_list_tests']).splitlines():
link(line, '%s.svg' % fnize(line))
benchmarks.append(
jobset.JobSpec(['perf', 'record', '-o', '%s-perf.data' % fnize(line),
'-g', '-F', '997',
'bins/mutrace/%s' % bm_name,
'--benchmark_filter=^%s$' % line,
'--benchmark_min_time=10'],
shortname='perf-%s' % fnize(line)))
profile_analysis.append(
jobset.JobSpec(['tools/run_tests/performance/process_local_perf_flamegraphs.sh'],
environ = {
'PERF_BASE_NAME': fnize(line),
'OUTPUT_DIR': 'reports',
'OUTPUT_FILENAME': fnize(line),
},
shortname='flame-%s' % fnize(line)))
cleanup.append(jobset.JobSpec(['rm', '%s-perf.data' % fnize(line)]))
cleanup.append(jobset.JobSpec(['rm', '%s-out.perf' % fnize(line)]))
# periodically flush out the list of jobs: temporary space required for this
# processing is large
if len(benchmarks) >= 20:
# run up to half the cpu count: each benchmark can use up to two cores
# (one for the microbenchmark, one for the data flush)
jobset.run(benchmarks, maxjobs=1)
jobset.run(profile_analysis, maxjobs=multiprocessing.cpu_count())
jobset.run(cleanup, maxjobs=multiprocessing.cpu_count())
benchmarks = []
profile_analysis = []
cleanup = []
# run the remaining benchmarks that weren't flushed
if len(benchmarks):
jobset.run(benchmarks, maxjobs=1)
jobset.run(profile_analysis, maxjobs=multiprocessing.cpu_count())
jobset.run(cleanup, maxjobs=multiprocessing.cpu_count())
def run_summary(bm_name, cfg, base_json_name):
subprocess.check_call(
['make', bm_name,
'CONFIG=%s' % cfg, '-j', '%d' % multiprocessing.cpu_count()])
cmd = ['bins/%s/%s' % (cfg, bm_name),
'--benchmark_out=%s.%s.json' % (base_json_name, cfg),
'--benchmark_out_format=json']
if args.summary_time is not None:
cmd += ['--benchmark_min_time=%d' % args.summary_time]
return subprocess.check_output(cmd)
def collect_summary(bm_name, args):
heading('Summary: %s [no counters]' % bm_name)
text(run_summary(bm_name, 'opt', bm_name))
heading('Summary: %s [with counters]' % bm_name)
text(run_summary(bm_name, 'counters', bm_name))
if args.bigquery_upload:
with open('%s.csv' % bm_name, 'w') as f:
f.write(subprocess.check_output(['tools/profiling/microbenchmarks/bm2bq.py',
'%s.counters.json' % bm_name,
'%s.opt.json' % bm_name]))
subprocess.check_call(['bq', 'load', 'microbenchmarks.microbenchmarks', '%s.csv' % bm_name])
collectors = {
'latency': collect_latency,
'perf': collect_perf,
'summary': collect_summary,
}
argp = argparse.ArgumentParser(description='Collect data from microbenchmarks')
argp.add_argument('-c', '--collect',
choices=sorted(collectors.keys()),
nargs='*',
default=sorted(collectors.keys()),
help='Which collectors should be run against each benchmark')
argp.add_argument('-b', '--benchmarks',
choices=bm_constants._AVAILABLE_BENCHMARK_TESTS,
default=bm_constants._AVAILABLE_BENCHMARK_TESTS,
nargs='+',
type=str,
help='Which microbenchmarks should be run')
argp.add_argument('--bigquery_upload',
default=False,
action='store_const',
const=True,
help='Upload results from summary collection to bigquery')
argp.add_argument('--summary_time',
default=None,
type=int,
help='Minimum time to run benchmarks for the summary collection')
args = argp.parse_args()
try:
for collect in args.collect:
for bm_name in args.benchmarks:
collectors[collect](bm_name, args)
finally:
if not os.path.exists('reports'):
os.makedirs('reports')
index_html += "</body>\n</html>\n"
with open('reports/index.html', 'w') as f:
f.write(index_html)