| #!/usr/bin/env python3 |
| # |
| # Script to analyze results of our branch prediction heuristics |
| # |
| # This file is part of GCC. |
| # |
| # GCC is free software; you can redistribute it and/or modify it under |
| # the terms of the GNU General Public License as published by the Free |
| # Software Foundation; either version 3, or (at your option) any later |
| # version. |
| # |
| # GCC is distributed in the hope that it will be useful, but WITHOUT ANY |
| # WARRANTY; without even the implied warranty of MERCHANTABILITY or |
| # FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License |
| # for more details. |
| # |
| # You should have received a copy of the GNU General Public License |
| # along with GCC; see the file COPYING3. If not see |
| # <http://www.gnu.org/licenses/>. */ |
| # |
| # |
| # |
| # This script is used to calculate two basic properties of the branch prediction |
| # heuristics - coverage and hitrate. Coverage is number of executions |
| # of a given branch matched by the heuristics and hitrate is probability |
| # that once branch is predicted as taken it is really taken. |
| # |
| # These values are useful to determine the quality of given heuristics. |
| # Hitrate may be directly used in predict.def. |
| # |
| # Usage: |
| # Step 1: Compile and profile your program. You need to use -fprofile-generate |
| # flag to get the profiles. |
| # Step 2: Make a reference run of the intrumented application. |
| # Step 3: Compile the program with collected profile and dump IPA profiles |
| # (-fprofile-use -fdump-ipa-profile-details) |
| # Step 4: Collect all generated dump files: |
| # find . -name '*.profile' | xargs cat > dump_file |
| # Step 5: Run the script: |
| # ./analyze_brprob.py dump_file |
| # and read results. Basically the following table is printed: |
| # |
| # HEURISTICS BRANCHES (REL) HITRATE COVERAGE (REL) |
| # early return (on trees) 3 0.2% 35.83% / 93.64% 66360 0.0% |
| # guess loop iv compare 8 0.6% 53.35% / 53.73% 11183344 0.0% |
| # call 18 1.4% 31.95% / 69.95% 51880179 0.2% |
| # loop guard 23 1.8% 84.13% / 84.85% 13749065956 42.2% |
| # opcode values positive (on trees) 42 3.3% 15.71% / 84.81% 6771097902 20.8% |
| # opcode values nonequal (on trees) 226 17.6% 72.48% / 72.84% 844753864 2.6% |
| # loop exit 231 18.0% 86.97% / 86.98% 8952666897 27.5% |
| # loop iterations 239 18.6% 91.10% / 91.10% 3062707264 9.4% |
| # DS theory 281 21.9% 82.08% / 83.39% 7787264075 23.9% |
| # no prediction 293 22.9% 46.92% / 70.70% 2293267840 7.0% |
| # guessed loop iterations 313 24.4% 76.41% / 76.41% 10782750177 33.1% |
| # first match 708 55.2% 82.30% / 82.31% 22489588691 69.0% |
| # combined 1282 100.0% 79.76% / 81.75% 32570120606 100.0% |
| # |
| # |
| # The heuristics called "first match" is a heuristics used by GCC branch |
| # prediction pass and it predicts 55.2% branches correctly. As you can, |
| # the heuristics has very good covertage (69.05%). On the other hand, |
| # "opcode values nonequal (on trees)" heuristics has good hirate, but poor |
| # coverage. |
| |
| import sys |
| import os |
| import re |
| import argparse |
| |
| from math import * |
| |
| counter_aggregates = set(['combined', 'first match', 'DS theory', |
| 'no prediction']) |
| |
| def percentage(a, b): |
| return 100.0 * a / b |
| |
| def average(values): |
| return 1.0 * sum(values) / len(values) |
| |
| def average_cutoff(values, cut): |
| l = len(values) |
| skip = floor(l * cut / 2) |
| if skip > 0: |
| values.sort() |
| values = values[skip:-skip] |
| return average(values) |
| |
| def median(values): |
| values.sort() |
| return values[int(len(values) / 2)] |
| |
| class Summary: |
| def __init__(self, name): |
| self.name = name |
| self.branches = 0 |
| self.successfull_branches = 0 |
| self.count = 0 |
| self.hits = 0 |
| self.fits = 0 |
| |
| def get_hitrate(self): |
| return 100.0 * self.hits / self.count |
| |
| def get_branch_hitrate(self): |
| return 100.0 * self.successfull_branches / self.branches |
| |
| def count_formatted(self): |
| v = self.count |
| for unit in ['','K','M','G','T','P','E','Z']: |
| if v < 1000: |
| return "%3.2f%s" % (v, unit) |
| v /= 1000.0 |
| return "%.1f%s" % (v, 'Y') |
| |
| def print(self, branches_max, count_max): |
| print('%-40s %8i %5.1f%% %11.2f%% %7.2f%% / %6.2f%% %14i %8s %5.1f%%' % |
| (self.name, self.branches, |
| percentage(self.branches, branches_max), |
| self.get_branch_hitrate(), |
| self.get_hitrate(), |
| percentage(self.fits, self.count), |
| self.count, self.count_formatted(), |
| percentage(self.count, count_max))) |
| |
| class Profile: |
| def __init__(self, filename): |
| self.filename = filename |
| self.heuristics = {} |
| self.niter_vector = [] |
| |
| def add(self, name, prediction, count, hits): |
| if not name in self.heuristics: |
| self.heuristics[name] = Summary(name) |
| |
| s = self.heuristics[name] |
| s.branches += 1 |
| |
| s.count += count |
| if prediction < 50: |
| hits = count - hits |
| remaining = count - hits |
| if hits >= remaining: |
| s.successfull_branches += 1 |
| |
| s.hits += hits |
| s.fits += max(hits, remaining) |
| |
| def add_loop_niter(self, niter): |
| if niter > 0: |
| self.niter_vector.append(niter) |
| |
| def branches_max(self): |
| return max([v.branches for k, v in self.heuristics.items()]) |
| |
| def count_max(self): |
| return max([v.count for k, v in self.heuristics.items()]) |
| |
| def print_group(self, sorting, group_name, heuristics): |
| count_max = self.count_max() |
| branches_max = self.branches_max() |
| |
| sorter = lambda x: x.branches |
| if sorting == 'branch-hitrate': |
| sorter = lambda x: x.get_branch_hitrate() |
| elif sorting == 'hitrate': |
| sorter = lambda x: x.get_hitrate() |
| elif sorting == 'coverage': |
| sorter = lambda x: x.count |
| elif sorting == 'name': |
| sorter = lambda x: x.name.lower() |
| |
| print('%-40s %8s %6s %12s %18s %14s %8s %6s' % |
| ('HEURISTICS', 'BRANCHES', '(REL)', |
| 'BR. HITRATE', 'HITRATE', 'COVERAGE', 'COVERAGE', '(REL)')) |
| for h in sorted(heuristics, key = sorter): |
| h.print(branches_max, count_max) |
| |
| def dump(self, sorting): |
| heuristics = self.heuristics.values() |
| if len(heuristics) == 0: |
| print('No heuristics available') |
| return |
| |
| special = list(filter(lambda x: x.name in counter_aggregates, |
| heuristics)) |
| normal = list(filter(lambda x: x.name not in counter_aggregates, |
| heuristics)) |
| |
| self.print_group(sorting, 'HEURISTICS', normal) |
| print() |
| self.print_group(sorting, 'HEURISTIC AGGREGATES', special) |
| |
| if len(self.niter_vector) > 0: |
| print ('\nLoop count: %d' % len(self.niter_vector)), |
| print(' avg. # of iter: %.2f' % average(self.niter_vector)) |
| print(' median # of iter: %.2f' % median(self.niter_vector)) |
| for v in [1, 5, 10, 20, 30]: |
| cut = 0.01 * v |
| print(' avg. (%d%% cutoff) # of iter: %.2f' |
| % (v, average_cutoff(self.niter_vector, cut))) |
| |
| parser = argparse.ArgumentParser() |
| parser.add_argument('dump_file', metavar = 'dump_file', |
| help = 'IPA profile dump file') |
| parser.add_argument('-s', '--sorting', dest = 'sorting', |
| choices = ['branches', 'branch-hitrate', 'hitrate', 'coverage', 'name'], |
| default = 'branches') |
| |
| args = parser.parse_args() |
| |
| profile = Profile(sys.argv[1]) |
| r = re.compile(' (.*) heuristics( of edge [0-9]*->[0-9]*)?( \\(.*\\))?: (.*)%.*exec ([0-9]*) hit ([0-9]*)') |
| loop_niter_str = ';; profile-based iteration count: ' |
| for l in open(args.dump_file).readlines(): |
| m = r.match(l) |
| if m != None and m.group(3) == None: |
| name = m.group(1) |
| prediction = float(m.group(4)) |
| count = int(m.group(5)) |
| hits = int(m.group(6)) |
| |
| profile.add(name, prediction, count, hits) |
| elif l.startswith(loop_niter_str): |
| v = int(l[len(loop_niter_str):]) |
| profile.add_loop_niter(v) |
| |
| profile.dump(args.sorting) |
