text: #!/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
  
  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',
                    default=['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_metadata',
                             'bm_fullstack_trickle',
                             ],
                    nargs='+',
                    type=str,
                    help='Which microbenchmarks should be run')
  argp.add_argument('--diff_perf',
                    default=None,
                    type=str,
                    help='Diff microbenchmarks against this git revision')
  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)
    if args.diff_perf:
      if 'summary' not in args.collect:
        for bm_name in args.benchmarks:
          run_summary(bm_name, 'opt', bm_name)
          run_summary(bm_name, 'counters', bm_name)
      where_am_i = subprocess.check_output(['git', 'rev-parse', '--abbrev-ref', 'HEAD']).strip()
      subprocess.check_call(['git', 'checkout', args.diff_perf])
      comparables = []
      subprocess.check_call(['make', 'clean'])
      try:
        for bm_name in args.benchmarks:
          try:
            run_summary(bm_name, 'opt', '%s.old' % bm_name)
            run_summary(bm_name, 'counters', '%s.old' % bm_name)
            comparables.append(bm_name)
          except subprocess.CalledProcessError, e:
            pass
      finally:
        subprocess.check_call(['git', 'checkout', where_am_i])
      for bm_name in comparables:
        diff = subprocess.check_output(['tools/profiling/microbenchmarks/bm_diff.py',
                                        '%s.counters.json' % bm_name,
                                        '%s.opt.json' % bm_name,
                                        '%s.old.counters.json' % bm_name,
                                        '%s.old.opt.json' % bm_name]).strip()
        if diff:
          heading('Performance diff: %s' % bm_name)
          text(diff)
  finally:
    index_html += "</body>\n</html>\n"
    with open('reports/index.html', 'w') as f:
      f.write(index_html)
  