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- #!/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'])
- hot_threshold = 10
- 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 PredictDefFile:
- def __init__(self, path):
- self.path = path
- self.predictors = {}
- def parse_and_modify(self, heuristics, write_def_file):
- lines = [x.rstrip() for x in open(self.path).readlines()]
- p = None
- modified_lines = []
- for i, l in enumerate(lines):
- if l.startswith('DEF_PREDICTOR'):
- next_line = lines[i + 1]
- if l.endswith(','):
- l += next_line
- m = re.match('.*"(.*)".*', l)
- p = m.group(1)
- elif l == '':
- p = None
- if p != None:
- heuristic = [x for x in heuristics if x.name == p]
- heuristic = heuristic[0] if len(heuristic) == 1 else None
- m = re.match('.*HITRATE \(([^)]*)\).*', l)
- if (m != None):
- self.predictors[p] = int(m.group(1))
- # modify the line
- if heuristic != None:
- new_line = (l[:m.start(1)]
- + str(round(heuristic.get_hitrate()))
- + l[m.end(1):])
- l = new_line
- p = None
- elif 'PROB_VERY_LIKELY' in l:
- self.predictors[p] = 100
- modified_lines.append(l)
- # save the file
- if write_def_file:
- with open(self.path, 'w+') as f:
- for l in modified_lines:
- f.write(l + '\n')
- class Heuristics:
- def __init__(self, count, hits, fits):
- self.count = count
- self.hits = hits
- self.fits = fits
- class Summary:
- def __init__(self, name):
- self.name = name
- self.edges= []
- def branches(self):
- return len(self.edges)
- def hits(self):
- return sum([x.hits for x in self.edges])
- def fits(self):
- return sum([x.fits for x in self.edges])
- def count(self):
- return sum([x.count for x in self.edges])
- def successfull_branches(self):
- return len([x for x in self.edges if 2 * x.hits >= x.count])
- 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', 'Y']:
- if v < 1000:
- return "%3.2f%s" % (v, unit)
- v /= 1000.0
- return "%.1f%s" % (v, 'Y')
- def count(self):
- return sum([x.count for x in self.edges])
- def print(self, branches_max, count_max, predict_def):
- # filter out most hot edges (if requested)
- self.edges = sorted(self.edges, reverse = True, key = lambda x: x.count)
- if args.coverage_threshold != None:
- threshold = args.coverage_threshold * self.count() / 100
- edges = [x for x in self.edges if x.count < threshold]
- if len(edges) != 0:
- self.edges = edges
- predicted_as = None
- if predict_def != None and self.name in predict_def.predictors:
- predicted_as = predict_def.predictors[self.name]
- 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)), end = '')
- if predicted_as != None:
- print('%12i%% %5.1f%%' % (predicted_as,
- self.get_hitrate() - predicted_as), end = '')
- else:
- print(' ' * 20, end = '')
- # print details about the most important edges
- if args.coverage_threshold == None:
- edges = [x for x in self.edges[:100] if x.count * hot_threshold > self.count()]
- if args.verbose:
- for c in edges:
- r = 100.0 * c.count / self.count()
- print(' %.0f%%:%d' % (r, c.count), end = '')
- elif len(edges) > 0:
- print(' %0.0f%%:%d' % (100.0 * sum([x.count for x in edges]) / self.count(), len(edges)), end = '')
- print()
- 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]
- if prediction < 50:
- hits = count - hits
- remaining = count - hits
- fits = max(hits, remaining)
- s.edges.append(Heuristics(count, hits, fits))
- 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, predict_def):
- 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 %12s %6s %s' %
- ('HEURISTICS', 'BRANCHES', '(REL)',
- 'BR. HITRATE', 'HITRATE', 'COVERAGE', 'COVERAGE', '(REL)',
- 'predict.def', '(REL)', 'HOT branches (>%d%%)' % hot_threshold))
- for h in sorted(heuristics, key = sorter):
- h.print(branches_max, count_max, predict_def)
- def dump(self, sorting):
- heuristics = self.heuristics.values()
- if len(heuristics) == 0:
- print('No heuristics available')
- return
- predict_def = None
- if args.def_file != None:
- predict_def = PredictDefFile(args.def_file)
- predict_def.parse_and_modify(heuristics, args.write_def_file)
- 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, predict_def)
- print()
- self.print_group(sorting, 'HEURISTIC AGGREGATES', special, predict_def)
- 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')
- parser.add_argument('-d', '--def-file', help = 'path to predict.def')
- parser.add_argument('-w', '--write-def-file', action = 'store_true',
- help = 'Modify predict.def file in order to set new numbers')
- parser.add_argument('-c', '--coverage-threshold', type = int,
- help = 'Ignore edges that have percentage coverage >= coverage-threshold')
- parser.add_argument('-v', '--verbose', action = 'store_true', help = 'Print verbose informations')
- args = parser.parse_args()
- profile = Profile(args.dump_file)
- loop_niter_str = ';; profile-based iteration count: '
- for l in open(args.dump_file):
- if l.startswith(';;heuristics;'):
- parts = l.strip().split(';')
- assert len(parts) == 8
- name = parts[3]
- prediction = float(parts[6])
- count = int(parts[4])
- hits = int(parts[5])
- 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)
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