analyze_brprob.py revision 1.1
1 #!/usr/bin/env python3 2 # 3 # Script to analyze results of our branch prediction heuristics 4 # 5 # This file is part of GCC. 6 # 7 # GCC is free software; you can redistribute it and/or modify it under 8 # the terms of the GNU General Public License as published by the Free 9 # Software Foundation; either version 3, or (at your option) any later 10 # version. 11 # 12 # GCC is distributed in the hope that it will be useful, but WITHOUT ANY 13 # WARRANTY; without even the implied warranty of MERCHANTABILITY or 14 # FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License 15 # for more details. 16 # 17 # You should have received a copy of the GNU General Public License 18 # along with GCC; see the file COPYING3. If not see 19 # <http://www.gnu.org/licenses/>. */ 20 # 21 # 22 # 23 # This script is used to calculate two basic properties of the branch prediction 24 # heuristics - coverage and hitrate. Coverage is number of executions 25 # of a given branch matched by the heuristics and hitrate is probability 26 # that once branch is predicted as taken it is really taken. 27 # 28 # These values are useful to determine the quality of given heuristics. 29 # Hitrate may be directly used in predict.def. 30 # 31 # Usage: 32 # Step 1: Compile and profile your program. You need to use -fprofile-generate 33 # flag to get the profiles. 34 # Step 2: Make a reference run of the intrumented application. 35 # Step 3: Compile the program with collected profile and dump IPA profiles 36 # (-fprofile-use -fdump-ipa-profile-details) 37 # Step 4: Collect all generated dump files: 38 # find . -name '*.profile' | xargs cat > dump_file 39 # Step 5: Run the script: 40 # ./analyze_brprob.py dump_file 41 # and read results. Basically the following table is printed: 42 # 43 # HEURISTICS BRANCHES (REL) HITRATE COVERAGE (REL) 44 # early return (on trees) 3 0.2% 35.83% / 93.64% 66360 0.0% 45 # guess loop iv compare 8 0.6% 53.35% / 53.73% 11183344 0.0% 46 # call 18 1.4% 31.95% / 69.95% 51880179 0.2% 47 # loop guard 23 1.8% 84.13% / 84.85% 13749065956 42.2% 48 # opcode values positive (on trees) 42 3.3% 15.71% / 84.81% 6771097902 20.8% 49 # opcode values nonequal (on trees) 226 17.6% 72.48% / 72.84% 844753864 2.6% 50 # loop exit 231 18.0% 86.97% / 86.98% 8952666897 27.5% 51 # loop iterations 239 18.6% 91.10% / 91.10% 3062707264 9.4% 52 # DS theory 281 21.9% 82.08% / 83.39% 7787264075 23.9% 53 # no prediction 293 22.9% 46.92% / 70.70% 2293267840 7.0% 54 # guessed loop iterations 313 24.4% 76.41% / 76.41% 10782750177 33.1% 55 # first match 708 55.2% 82.30% / 82.31% 22489588691 69.0% 56 # combined 1282 100.0% 79.76% / 81.75% 32570120606 100.0% 57 # 58 # 59 # The heuristics called "first match" is a heuristics used by GCC branch 60 # prediction pass and it predicts 55.2% branches correctly. As you can, 61 # the heuristics has very good covertage (69.05%). On the other hand, 62 # "opcode values nonequal (on trees)" heuristics has good hirate, but poor 63 # coverage. 64 65 import sys 66 import os 67 import re 68 import argparse 69 70 from math import * 71 72 counter_aggregates = set(['combined', 'first match', 'DS theory', 73 'no prediction']) 74 75 def percentage(a, b): 76 return 100.0 * a / b 77 78 def average(values): 79 return 1.0 * sum(values) / len(values) 80 81 def average_cutoff(values, cut): 82 l = len(values) 83 skip = floor(l * cut / 2) 84 if skip > 0: 85 values.sort() 86 values = values[skip:-skip] 87 return average(values) 88 89 def median(values): 90 values.sort() 91 return values[int(len(values) / 2)] 92 93 class Summary: 94 def __init__(self, name): 95 self.name = name 96 self.branches = 0 97 self.successfull_branches = 0 98 self.count = 0 99 self.hits = 0 100 self.fits = 0 101 102 def get_hitrate(self): 103 return 100.0 * self.hits / self.count 104 105 def get_branch_hitrate(self): 106 return 100.0 * self.successfull_branches / self.branches 107 108 def count_formatted(self): 109 v = self.count 110 for unit in ['','K','M','G','T','P','E','Z']: 111 if v < 1000: 112 return "%3.2f%s" % (v, unit) 113 v /= 1000.0 114 return "%.1f%s" % (v, 'Y') 115 116 def print(self, branches_max, count_max): 117 print('%-40s %8i %5.1f%% %11.2f%% %7.2f%% / %6.2f%% %14i %8s %5.1f%%' % 118 (self.name, self.branches, 119 percentage(self.branches, branches_max), 120 self.get_branch_hitrate(), 121 self.get_hitrate(), 122 percentage(self.fits, self.count), 123 self.count, self.count_formatted(), 124 percentage(self.count, count_max))) 125 126 class Profile: 127 def __init__(self, filename): 128 self.filename = filename 129 self.heuristics = {} 130 self.niter_vector = [] 131 132 def add(self, name, prediction, count, hits): 133 if not name in self.heuristics: 134 self.heuristics[name] = Summary(name) 135 136 s = self.heuristics[name] 137 s.branches += 1 138 139 s.count += count 140 if prediction < 50: 141 hits = count - hits 142 remaining = count - hits 143 if hits >= remaining: 144 s.successfull_branches += 1 145 146 s.hits += hits 147 s.fits += max(hits, remaining) 148 149 def add_loop_niter(self, niter): 150 if niter > 0: 151 self.niter_vector.append(niter) 152 153 def branches_max(self): 154 return max([v.branches for k, v in self.heuristics.items()]) 155 156 def count_max(self): 157 return max([v.count for k, v in self.heuristics.items()]) 158 159 def print_group(self, sorting, group_name, heuristics): 160 count_max = self.count_max() 161 branches_max = self.branches_max() 162 163 sorter = lambda x: x.branches 164 if sorting == 'branch-hitrate': 165 sorter = lambda x: x.get_branch_hitrate() 166 elif sorting == 'hitrate': 167 sorter = lambda x: x.get_hitrate() 168 elif sorting == 'coverage': 169 sorter = lambda x: x.count 170 elif sorting == 'name': 171 sorter = lambda x: x.name.lower() 172 173 print('%-40s %8s %6s %12s %18s %14s %8s %6s' % 174 ('HEURISTICS', 'BRANCHES', '(REL)', 175 'BR. HITRATE', 'HITRATE', 'COVERAGE', 'COVERAGE', '(REL)')) 176 for h in sorted(heuristics, key = sorter): 177 h.print(branches_max, count_max) 178 179 def dump(self, sorting): 180 heuristics = self.heuristics.values() 181 if len(heuristics) == 0: 182 print('No heuristics available') 183 return 184 185 special = list(filter(lambda x: x.name in counter_aggregates, 186 heuristics)) 187 normal = list(filter(lambda x: x.name not in counter_aggregates, 188 heuristics)) 189 190 self.print_group(sorting, 'HEURISTICS', normal) 191 print() 192 self.print_group(sorting, 'HEURISTIC AGGREGATES', special) 193 194 if len(self.niter_vector) > 0: 195 print ('\nLoop count: %d' % len(self.niter_vector)), 196 print(' avg. # of iter: %.2f' % average(self.niter_vector)) 197 print(' median # of iter: %.2f' % median(self.niter_vector)) 198 for v in [1, 5, 10, 20, 30]: 199 cut = 0.01 * v 200 print(' avg. (%d%% cutoff) # of iter: %.2f' 201 % (v, average_cutoff(self.niter_vector, cut))) 202 203 parser = argparse.ArgumentParser() 204 parser.add_argument('dump_file', metavar = 'dump_file', 205 help = 'IPA profile dump file') 206 parser.add_argument('-s', '--sorting', dest = 'sorting', 207 choices = ['branches', 'branch-hitrate', 'hitrate', 'coverage', 'name'], 208 default = 'branches') 209 210 args = parser.parse_args() 211 212 profile = Profile(sys.argv[1]) 213 r = re.compile(' (.*) heuristics( of edge [0-9]*->[0-9]*)?( \\(.*\\))?: (.*)%.*exec ([0-9]*) hit ([0-9]*)') 214 loop_niter_str = ';; profile-based iteration count: ' 215 for l in open(args.dump_file).readlines(): 216 m = r.match(l) 217 if m != None and m.group(3) == None: 218 name = m.group(1) 219 prediction = float(m.group(4)) 220 count = int(m.group(5)) 221 hits = int(m.group(6)) 222 223 profile.add(name, prediction, count, hits) 224 elif l.startswith(loop_niter_str): 225 v = int(l[len(loop_niter_str):]) 226 profile.add_loop_niter(v) 227 228 profile.dump(args.sorting) 229
Indexes created Sun Apr 26 00:22:38 UTC 2026