mirror of git://gcc.gnu.org/git/gcc.git
229 lines
8.3 KiB
Python
Executable File
229 lines
8.3 KiB
Python
Executable File
#!/usr/bin/env python3
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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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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 Summary:
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def __init__(self, name):
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self.name = name
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self.branches = 0
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self.successfull_branches = 0
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self.count = 0
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self.hits = 0
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self.fits = 0
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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']:
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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 print(self, branches_max, count_max):
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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)))
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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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s.branches += 1
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s.count += count
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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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if hits >= remaining:
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s.successfull_branches += 1
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s.hits += hits
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s.fits += max(hits, remaining)
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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):
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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' %
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('HEURISTICS', 'BRANCHES', '(REL)',
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'BR. HITRATE', 'HITRATE', 'COVERAGE', 'COVERAGE', '(REL)'))
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for h in sorted(heuristics, key = sorter):
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h.print(branches_max, count_max)
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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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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)
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print()
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self.print_group(sorting, 'HEURISTIC AGGREGATES', special)
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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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args = parser.parse_args()
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profile = Profile(sys.argv[1])
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r = re.compile(' (.*) heuristics( of edge [0-9]*->[0-9]*)?( \\(.*\\))?: (.*)%.*exec ([0-9]*) hit ([0-9]*)')
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loop_niter_str = ';; profile-based iteration count: '
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for l in open(args.dump_file).readlines():
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m = r.match(l)
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if m != None and m.group(3) == None:
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name = m.group(1)
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prediction = float(m.group(4))
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count = int(m.group(5))
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hits = int(m.group(6))
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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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