|
|
|
|
#!/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.
|
|
|
|
|
|
|
|
|
|
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
|
|
|
|
|
|
|
|
|
|
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')
|
|
|
|
|
|
|
|
|
|
port_server_port = 32766
|
|
|
|
|
start_port_server.start_port_server(port_server_port)
|
|
|
|
|
|
|
|
|
|
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" % (tgt, txt)
|
|
|
|
|
|
|
|
|
|
def text(txt):
|
|
|
|
|
global index_html
|
|
|
|
|
index_html += "<p><pre>%s</pre></p>" % 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],
|
|
|
|
|
environ={'LATENCY_TRACE': '%s.trace' % 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=None))
|
|
|
|
|
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(4, 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),
|
|
|
|
|
add_env={'GRPC_TEST_PORT_SERVER': 'localhost:%d' % port_server_port})
|
|
|
|
|
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),
|
|
|
|
|
add_env={'GRPC_TEST_PORT_SERVER': 'localhost:%d' % port_server_port})
|
|
|
|
|
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()])
|
|
|
|
|
for line in subprocess.check_output(['bins/mutrace/%s' % bm_name,
|
|
|
|
|
'--benchmark_list_tests']).splitlines():
|
|
|
|
|
subprocess.check_call(['sudo', 'perf', 'record', '-g', '-c', '1000',
|
|
|
|
|
'bins/mutrace/%s' % bm_name,
|
|
|
|
|
'--benchmark_filter=^%s$' % line,
|
|
|
|
|
'--benchmark_min_time=20'])
|
|
|
|
|
with open('/tmp/bm.perf', 'w') as f:
|
|
|
|
|
f.write(subprocess.check_output(['sudo', 'perf', 'script']))
|
|
|
|
|
with open('/tmp/bm.folded', 'w') as f:
|
|
|
|
|
f.write(subprocess.check_output([
|
|
|
|
|
'%s/stackcollapse-perf.pl' % flamegraph_dir, '/tmp/bm.perf']))
|
|
|
|
|
link(line, '%s.svg' % fnize(line))
|
|
|
|
|
with open('reports/%s.svg' % fnize(line), 'w') as f:
|
|
|
|
|
f.write(subprocess.check_output([
|
|
|
|
|
'%s/flamegraph.pl' % flamegraph_dir, '/tmp/bm.folded']))
|
|
|
|
|
|
|
|
|
|
def collect_summary(bm_name, args):
|
|
|
|
|
heading('Summary: %s' % bm_name)
|
|
|
|
|
subprocess.check_call(
|
|
|
|
|
['make', bm_name,
|
|
|
|
|
'CONFIG=counters', '-j', '%d' % multiprocessing.cpu_count()])
|
|
|
|
|
text(subprocess.check_output(['bins/counters/%s' % bm_name,
|
|
|
|
|
'--benchmark_out=out.json',
|
|
|
|
|
'--benchmark_out_format=json']))
|
|
|
|
|
if args.bigquery_upload:
|
|
|
|
|
with open('/tmp/out.csv', 'w') as f:
|
|
|
|
|
f.write(subprocess.check_output(['tools/profiling/microbenchmarks/bm2bq.py out.json']))
|
|
|
|
|
subprocess.check_call(['bq', 'load', 'microbenchmarks.microbenchmarks', 'out.csv'])
|
|
|
|
|
|
|
|
|
|
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',
|
|
|
|
|
default=['bm_fullstack'],
|
|
|
|
|
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')
|
|
|
|
|
args = argp.parse_args()
|
|
|
|
|
|
|
|
|
|
for bm_name in args.benchmarks:
|
|
|
|
|
for collect in args.collect:
|
|
|
|
|
collectors[collect](bm_name, args)
|
|
|
|
|
|
|
|
|
|
index_html += "</body>\n</html>\n"
|
|
|
|
|
with open('reports/index.html', 'w') as f:
|
|
|
|
|
f.write(index_html)
|
