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#!/usr/bin/env python
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# Copyright 2017, Google Inc.
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# All rights reserved.
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are
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# met:
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#
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# * Redistributions of source code must retain the above copyright
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# notice, this list of conditions and the following disclaimer.
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# * Redistributions in binary form must reproduce the above
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# copyright notice, this list of conditions and the following disclaimer
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# in the documentation and/or other materials provided with the
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# distribution.
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# * Neither the name of Google Inc. nor the names of its
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# contributors may be used to endorse or promote products derived from
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# this software without specific prior written permission.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
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# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
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# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
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# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
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# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
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# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
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# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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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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_AVAILABLE_BENCHMARK_TESTS = ['bm_fullstack_unary_ping_pong',
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'bm_fullstack_streaming_ping_pong',
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'bm_fullstack_streaming_pump',
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'bm_closure',
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'bm_cq',
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'bm_call_create',
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'bm_error',
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'bm_chttp2_hpack',
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'bm_chttp2_transport',
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'bm_pollset',
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'bm_metadata',
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'bm_fullstack_trickle']
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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,
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'CONFIG=basicprof', '-j', '%d' % multiprocessing.cpu_count()])
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for line in subprocess.check_output(['bins/basicprof/%s' % bm_name,
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'--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(['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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environ={'LATENCY_TRACE': '%s.trace' % fnize(line)}))
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profile_analysis.append(
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jobset.JobSpec([sys.executable,
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'tools/profiling/latency_profile/profile_analyzer.py',
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'--source', '%s.trace' % fnize(line), '--fmt', 'simple',
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'--out', 'reports/%s.txt' % fnize(line)], timeout_seconds=None))
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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(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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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,
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'CONFIG=mutrace', '-j', '%d' % multiprocessing.cpu_count()])
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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(['bins/mutrace/%s' % bm_name,
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'--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(['perf', 'record', '-o', '%s-perf.data' % fnize(line),
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'-g', '-F', '997',
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'bins/mutrace/%s' % bm_name,
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'--benchmark_filter=^%s$' % line,
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'--benchmark_min_time=10']))
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profile_analysis.append(
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jobset.JobSpec(['tools/run_tests/performance/process_local_perf_flamegraphs.sh'],
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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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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', '%d' % multiprocessing.cpu_count()])
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cmd = ['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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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(subprocess.check_output(['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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subprocess.check_call(['bq', 'load', 'microbenchmarks.microbenchmarks', '%s.csv' % bm_name])
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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('-c', '--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('-b', '--benchmarks',
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choices=_AVAILABLE_BENCHMARK_TESTS,
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default=_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('--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('--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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