137 lines
5.4 KiB
Python
137 lines
5.4 KiB
Python
#!/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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def percentage(a, b):
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return 100.0 * a / b
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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.count = 0
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self.hits = 0
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self.fits = 0
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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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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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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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s.hits += hits
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s.fits += max(hits, count - hits)
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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 dump(self):
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print('%-36s %8s %6s %-16s %14s %8s %6s' % ('HEURISTICS', 'BRANCHES', '(REL)',
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'HITRATE', 'COVERAGE', 'COVERAGE', '(REL)'))
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for (k, v) in sorted(self.heuristics.items(), key = lambda x: x[1].branches):
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print('%-36s %8i %5.1f%% %6.2f%% / %6.2f%% %14i %8s %5.1f%%' %
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(k, v.branches, percentage(v.branches, self.branches_max ()),
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percentage(v.hits, v.count), percentage(v.fits, v.count),
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v.count, v.count_formatted(), percentage(v.count, self.count_max()) ))
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if len(sys.argv) != 2:
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print('Usage: ./analyze_brprob.py dump_file')
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exit(1)
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profile = Profile(sys.argv[1])
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r = re.compile(' (.*) heuristics: (.*)%.*exec ([0-9]*) hit ([0-9]*)')
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for l in open(profile.filename).readlines():
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m = r.match(l)
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if m != None:
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name = m.group(1)
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prediction = float(m.group(2))
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count = int(m.group(3))
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hits = int(m.group(4))
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profile.add(name, prediction, count, hits)
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profile.dump()
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