tools/run_tests/run_microbenchmark.py - platform/external/grpc-grpc - Gitiles

 #!/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)
	#!/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)