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