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

 #!/usr/bin/env python
 # 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 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

 _AVAILABLE_BENCHMARK_TESTS = ['bm_fullstack_unary_ping_pong',
                               'bm_fullstack_streaming_ping_pong',
                               'bm_fullstack_streaming_pump',
                               'bm_closure',
                               'bm_cq',
                               'bm_call_create',
                               'bm_error',
                               'bm_chttp2_hpack',
                               'bm_chttp2_transport',
                               'bm_pollset',
                               'bm_metadata',
                               'bm_fullstack_trickle']

 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)}))
     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(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']))
     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),
                        }))
     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=_AVAILABLE_BENCHMARK_TESTS,
                   default=_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, 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 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

	_AVAILABLE_BENCHMARK_TESTS = ['bm_fullstack_unary_ping_pong',
	'bm_fullstack_streaming_ping_pong',
	'bm_fullstack_streaming_pump',
	'bm_closure',
	'bm_cq',
	'bm_call_create',
	'bm_error',
	'bm_chttp2_hpack',
	'bm_chttp2_transport',
	'bm_pollset',
	'bm_metadata',
	'bm_fullstack_trickle']

	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)}))
	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(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']))
	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),
	}))
	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=_AVAILABLE_BENCHMARK_TESTS,
	default=_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)