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contrib/ChangeLog: * analyze_brprob.py: Add copyright header. * analyze_brprob_spec.py: Likewise. * check-params-in-docs.py: Likewise. * check_GNU_style.py: Likewise. * check_GNU_style_lib.py: Likewise. * filter-clang-warnings.py: Likewise. * gcc-changelog/git_check_commit.py: Likewise. * gcc-changelog/git_commit.py: Likewise. * gcc-changelog/git_email.py: Likewise. * gcc-changelog/git_repository.py: Likewise. * gcc-changelog/git_update_version.py: Likewise. * gcc-changelog/test_email.py: Likewise. * mark_spam.py: Likewise.
335 lines
12 KiB
Python
Executable File
335 lines
12 KiB
Python
Executable File
#!/usr/bin/env python3
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# Copyright (C) 2016 Free Software Foundation, Inc.
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#
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# Script to analyze results of our branch prediction heuristics
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#
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# This file is part of GCC.
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#
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# GCC is free software; you can redistribute it and/or modify it under
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# the terms of the GNU General Public License as published by the Free
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# Software Foundation; either version 3, or (at your option) any later
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# version.
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#
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# GCC is distributed in the hope that it will be useful, but WITHOUT ANY
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# WARRANTY; without even the implied warranty of MERCHANTABILITY or
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# FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License
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# for more details.
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#
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# You should have received a copy of the GNU General Public License
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# along with GCC; see the file COPYING3. If not see
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# <http://www.gnu.org/licenses/>. */
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#
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#
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#
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# This script is used to calculate two basic properties of the branch prediction
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# heuristics - coverage and hitrate. Coverage is number of executions
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# of a given branch matched by the heuristics and hitrate is probability
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# that once branch is predicted as taken it is really taken.
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#
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# These values are useful to determine the quality of given heuristics.
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# Hitrate may be directly used in predict.def.
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#
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# Usage:
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# Step 1: Compile and profile your program. You need to use -fprofile-generate
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# flag to get the profiles.
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# Step 2: Make a reference run of the intrumented application.
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# Step 3: Compile the program with collected profile and dump IPA profiles
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# (-fprofile-use -fdump-ipa-profile-details)
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# Step 4: Collect all generated dump files:
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# find . -name '*.profile' | xargs cat > dump_file
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# Step 5: Run the script:
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# ./analyze_brprob.py dump_file
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# and read results. Basically the following table is printed:
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#
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# HEURISTICS BRANCHES (REL) HITRATE COVERAGE (REL)
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# early return (on trees) 3 0.2% 35.83% / 93.64% 66360 0.0%
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# guess loop iv compare 8 0.6% 53.35% / 53.73% 11183344 0.0%
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# call 18 1.4% 31.95% / 69.95% 51880179 0.2%
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# loop guard 23 1.8% 84.13% / 84.85% 13749065956 42.2%
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# opcode values positive (on trees) 42 3.3% 15.71% / 84.81% 6771097902 20.8%
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# opcode values nonequal (on trees) 226 17.6% 72.48% / 72.84% 844753864 2.6%
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# loop exit 231 18.0% 86.97% / 86.98% 8952666897 27.5%
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# loop iterations 239 18.6% 91.10% / 91.10% 3062707264 9.4%
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# DS theory 281 21.9% 82.08% / 83.39% 7787264075 23.9%
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# no prediction 293 22.9% 46.92% / 70.70% 2293267840 7.0%
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# guessed loop iterations 313 24.4% 76.41% / 76.41% 10782750177 33.1%
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# first match 708 55.2% 82.30% / 82.31% 22489588691 69.0%
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# combined 1282 100.0% 79.76% / 81.75% 32570120606 100.0%
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#
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#
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# The heuristics called "first match" is a heuristics used by GCC branch
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# prediction pass and it predicts 55.2% branches correctly. As you can,
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# the heuristics has very good covertage (69.05%). On the other hand,
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# "opcode values nonequal (on trees)" heuristics has good hirate, but poor
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# coverage.
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import sys
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import os
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import re
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import argparse
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from math import *
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counter_aggregates = set(['combined', 'first match', 'DS theory',
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'no prediction'])
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hot_threshold = 10
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def percentage(a, b):
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return 100.0 * a / b
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def average(values):
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return 1.0 * sum(values) / len(values)
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def average_cutoff(values, cut):
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l = len(values)
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skip = floor(l * cut / 2)
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if skip > 0:
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values.sort()
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values = values[skip:-skip]
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return average(values)
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def median(values):
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values.sort()
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return values[int(len(values) / 2)]
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class PredictDefFile:
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def __init__(self, path):
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self.path = path
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self.predictors = {}
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def parse_and_modify(self, heuristics, write_def_file):
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lines = [x.rstrip() for x in open(self.path).readlines()]
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p = None
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modified_lines = []
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for i, l in enumerate(lines):
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if l.startswith('DEF_PREDICTOR'):
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next_line = lines[i + 1]
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if l.endswith(','):
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l += next_line
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m = re.match('.*"(.*)".*', l)
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p = m.group(1)
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elif l == '':
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p = None
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if p != None:
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heuristic = [x for x in heuristics if x.name == p]
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heuristic = heuristic[0] if len(heuristic) == 1 else None
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m = re.match('.*HITRATE \(([^)]*)\).*', l)
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if (m != None):
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self.predictors[p] = int(m.group(1))
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# modify the line
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if heuristic != None:
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new_line = (l[:m.start(1)]
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+ str(round(heuristic.get_hitrate()))
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+ l[m.end(1):])
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l = new_line
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p = None
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elif 'PROB_VERY_LIKELY' in l:
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self.predictors[p] = 100
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modified_lines.append(l)
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# save the file
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if write_def_file:
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with open(self.path, 'w+') as f:
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for l in modified_lines:
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f.write(l + '\n')
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class Heuristics:
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def __init__(self, count, hits, fits):
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self.count = count
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self.hits = hits
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self.fits = fits
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class Summary:
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def __init__(self, name):
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self.name = name
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self.edges= []
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def branches(self):
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return len(self.edges)
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def hits(self):
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return sum([x.hits for x in self.edges])
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def fits(self):
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return sum([x.fits for x in self.edges])
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def count(self):
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return sum([x.count for x in self.edges])
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def successfull_branches(self):
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return len([x for x in self.edges if 2 * x.hits >= x.count])
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def get_hitrate(self):
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return 100.0 * self.hits() / self.count()
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def get_branch_hitrate(self):
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return 100.0 * self.successfull_branches() / self.branches()
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def count_formatted(self):
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v = self.count()
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for unit in ['', 'k', 'M', 'G', 'T', 'P', 'E', 'Z', 'Y']:
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if v < 1000:
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return "%3.2f%s" % (v, unit)
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v /= 1000.0
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return "%.1f%s" % (v, 'Y')
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def count(self):
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return sum([x.count for x in self.edges])
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def print(self, branches_max, count_max, predict_def):
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# filter out most hot edges (if requested)
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self.edges = sorted(self.edges, reverse = True, key = lambda x: x.count)
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if args.coverage_threshold != None:
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threshold = args.coverage_threshold * self.count() / 100
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edges = [x for x in self.edges if x.count < threshold]
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if len(edges) != 0:
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self.edges = edges
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predicted_as = None
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if predict_def != None and self.name in predict_def.predictors:
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predicted_as = predict_def.predictors[self.name]
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print('%-40s %8i %5.1f%% %11.2f%% %7.2f%% / %6.2f%% %14i %8s %5.1f%%' %
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(self.name, self.branches(),
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percentage(self.branches(), branches_max),
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self.get_branch_hitrate(),
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self.get_hitrate(),
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percentage(self.fits(), self.count()),
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self.count(), self.count_formatted(),
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percentage(self.count(), count_max)), end = '')
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if predicted_as != None:
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print('%12i%% %5.1f%%' % (predicted_as,
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self.get_hitrate() - predicted_as), end = '')
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else:
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print(' ' * 20, end = '')
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# print details about the most important edges
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if args.coverage_threshold == None:
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edges = [x for x in self.edges[:100] if x.count * hot_threshold > self.count()]
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if args.verbose:
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for c in edges:
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r = 100.0 * c.count / self.count()
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print(' %.0f%%:%d' % (r, c.count), end = '')
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elif len(edges) > 0:
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print(' %0.0f%%:%d' % (100.0 * sum([x.count for x in edges]) / self.count(), len(edges)), end = '')
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print()
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class Profile:
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def __init__(self, filename):
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self.filename = filename
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self.heuristics = {}
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self.niter_vector = []
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def add(self, name, prediction, count, hits):
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if not name in self.heuristics:
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self.heuristics[name] = Summary(name)
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s = self.heuristics[name]
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if prediction < 50:
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hits = count - hits
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remaining = count - hits
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fits = max(hits, remaining)
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s.edges.append(Heuristics(count, hits, fits))
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def add_loop_niter(self, niter):
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if niter > 0:
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self.niter_vector.append(niter)
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def branches_max(self):
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return max([v.branches() for k, v in self.heuristics.items()])
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def count_max(self):
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return max([v.count() for k, v in self.heuristics.items()])
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def print_group(self, sorting, group_name, heuristics, predict_def):
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count_max = self.count_max()
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branches_max = self.branches_max()
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sorter = lambda x: x.branches()
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if sorting == 'branch-hitrate':
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sorter = lambda x: x.get_branch_hitrate()
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elif sorting == 'hitrate':
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sorter = lambda x: x.get_hitrate()
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elif sorting == 'coverage':
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sorter = lambda x: x.count
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elif sorting == 'name':
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sorter = lambda x: x.name.lower()
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print('%-40s %8s %6s %12s %18s %14s %8s %6s %12s %6s %s' %
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('HEURISTICS', 'BRANCHES', '(REL)',
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'BR. HITRATE', 'HITRATE', 'COVERAGE', 'COVERAGE', '(REL)',
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'predict.def', '(REL)', 'HOT branches (>%d%%)' % hot_threshold))
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for h in sorted(heuristics, key = sorter):
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h.print(branches_max, count_max, predict_def)
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def dump(self, sorting):
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heuristics = self.heuristics.values()
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if len(heuristics) == 0:
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print('No heuristics available')
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return
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predict_def = None
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if args.def_file != None:
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predict_def = PredictDefFile(args.def_file)
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predict_def.parse_and_modify(heuristics, args.write_def_file)
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special = list(filter(lambda x: x.name in counter_aggregates,
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heuristics))
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normal = list(filter(lambda x: x.name not in counter_aggregates,
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heuristics))
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self.print_group(sorting, 'HEURISTICS', normal, predict_def)
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print()
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self.print_group(sorting, 'HEURISTIC AGGREGATES', special, predict_def)
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if len(self.niter_vector) > 0:
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print ('\nLoop count: %d' % len(self.niter_vector)),
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print(' avg. # of iter: %.2f' % average(self.niter_vector))
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print(' median # of iter: %.2f' % median(self.niter_vector))
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for v in [1, 5, 10, 20, 30]:
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cut = 0.01 * v
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print(' avg. (%d%% cutoff) # of iter: %.2f'
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% (v, average_cutoff(self.niter_vector, cut)))
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parser = argparse.ArgumentParser()
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parser.add_argument('dump_file', metavar = 'dump_file',
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help = 'IPA profile dump file')
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parser.add_argument('-s', '--sorting', dest = 'sorting',
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choices = ['branches', 'branch-hitrate', 'hitrate', 'coverage', 'name'],
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default = 'branches')
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parser.add_argument('-d', '--def-file', help = 'path to predict.def')
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parser.add_argument('-w', '--write-def-file', action = 'store_true',
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help = 'Modify predict.def file in order to set new numbers')
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parser.add_argument('-c', '--coverage-threshold', type = int,
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help = 'Ignore edges that have percentage coverage >= coverage-threshold')
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parser.add_argument('-v', '--verbose', action = 'store_true', help = 'Print verbose informations')
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args = parser.parse_args()
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profile = Profile(args.dump_file)
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loop_niter_str = ';; profile-based iteration count: '
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for l in open(args.dump_file):
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if l.startswith(';;heuristics;'):
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parts = l.strip().split(';')
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assert len(parts) == 8
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name = parts[3]
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prediction = float(parts[6])
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count = int(parts[4])
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hits = int(parts[5])
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profile.add(name, prediction, count, hits)
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elif l.startswith(loop_niter_str):
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v = int(l[len(loop_niter_str):])
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profile.add_loop_niter(v)
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profile.dump(args.sorting)
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