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 65import sys 66import os 67import re 68import argparse 69 70from math import * 71 72counter_aggregates = set(['combined', 'first match', 'DS theory', 73 'no prediction']) 74hot_threshold = 10 75 76def percentage(a, b): 77 return 100.0 * a / b 78 79def average(values): 80 return 1.0 * sum(values) / len(values) 81 82def average_cutoff(values, cut): 83 l = len(values) 84 skip = floor(l * cut / 2) 85 if skip > 0: 86 values.sort() 87 values = values[skip:-skip] 88 return average(values) 89 90def median(values): 91 values.sort() 92 return values[int(len(values) / 2)] 93 94class PredictDefFile: 95 def __init__(self, path): 96 self.path = path 97 self.predictors = {} 98 99 def parse_and_modify(self, heuristics, write_def_file): 100 lines = [x.rstrip() for x in open(self.path).readlines()] 101 102 p = None 103 modified_lines = [] 104 for i, l in enumerate(lines): 105 if l.startswith('DEF_PREDICTOR'): 106 next_line = lines[i + 1] 107 if l.endswith(','): 108 l += next_line 109 m = re.match('.*"(.*)".*', l) 110 p = m.group(1) 111 elif l == '': 112 p = None 113 114 if p != None: 115 heuristic = [x for x in heuristics if x.name == p] 116 heuristic = heuristic[0] if len(heuristic) == 1 else None 117 118 m = re.match('.*HITRATE \(([^)]*)\).*', l) 119 if (m != None): 120 self.predictors[p] = int(m.group(1)) 121 122 # modify the line 123 if heuristic != None: 124 new_line = (l[:m.start(1)] 125 + str(round(heuristic.get_hitrate())) 126 + l[m.end(1):]) 127 l = new_line 128 p = None 129 elif 'PROB_VERY_LIKELY' in l: 130 self.predictors[p] = 100 131 modified_lines.append(l) 132 133 # save the file 134 if write_def_file: 135 with open(self.path, 'w+') as f: 136 for l in modified_lines: 137 f.write(l + '\n') 138class Heuristics: 139 def __init__(self, count, hits, fits): 140 self.count = count 141 self.hits = hits 142 self.fits = fits 143 144class Summary: 145 def __init__(self, name): 146 self.name = name 147 self.edges= [] 148 149 def branches(self): 150 return len(self.edges) 151 152 def hits(self): 153 return sum([x.hits for x in self.edges]) 154 155 def fits(self): 156 return sum([x.fits for x in self.edges]) 157 158 def count(self): 159 return sum([x.count for x in self.edges]) 160 161 def successfull_branches(self): 162 return len([x for x in self.edges if 2 * x.hits >= x.count]) 163 164 def get_hitrate(self): 165 return 100.0 * self.hits() / self.count() 166 167 def get_branch_hitrate(self): 168 return 100.0 * self.successfull_branches() / self.branches() 169 170 def count_formatted(self): 171 v = self.count() 172 for unit in ['', 'k', 'M', 'G', 'T', 'P', 'E', 'Z', 'Y']: 173 if v < 1000: 174 return "%3.2f%s" % (v, unit) 175 v /= 1000.0 176 return "%.1f%s" % (v, 'Y') 177 178 def count(self): 179 return sum([x.count for x in self.edges]) 180 181 def print(self, branches_max, count_max, predict_def): 182 # filter out most hot edges (if requested) 183 self.edges = sorted(self.edges, reverse = True, key = lambda x: x.count) 184 if args.coverage_threshold != None: 185 threshold = args.coverage_threshold * self.count() / 100 186 edges = [x for x in self.edges if x.count < threshold] 187 if len(edges) != 0: 188 self.edges = edges 189 190 predicted_as = None 191 if predict_def != None and self.name in predict_def.predictors: 192 predicted_as = predict_def.predictors[self.name] 193 194 print('%-40s %8i %5.1f%% %11.2f%% %7.2f%% / %6.2f%% %14i %8s %5.1f%%' % 195 (self.name, self.branches(), 196 percentage(self.branches(), branches_max), 197 self.get_branch_hitrate(), 198 self.get_hitrate(), 199 percentage(self.fits(), self.count()), 200 self.count(), self.count_formatted(), 201 percentage(self.count(), count_max)), end = '') 202 203 if predicted_as != None: 204 print('%12i%% %5.1f%%' % (predicted_as, 205 self.get_hitrate() - predicted_as), end = '') 206 else: 207 print(' ' * 20, end = '') 208 209 # print details about the most important edges 210 if args.coverage_threshold == None: 211 edges = [x for x in self.edges[:100] if x.count * hot_threshold > self.count()] 212 if args.verbose: 213 for c in edges: 214 r = 100.0 * c.count / self.count() 215 print(' %.0f%%:%d' % (r, c.count), end = '') 216 elif len(edges) > 0: 217 print(' %0.0f%%:%d' % (100.0 * sum([x.count for x in edges]) / self.count(), len(edges)), end = '') 218 219 print() 220 221class Profile: 222 def __init__(self, filename): 223 self.filename = filename 224 self.heuristics = {} 225 self.niter_vector = [] 226 227 def add(self, name, prediction, count, hits): 228 if not name in self.heuristics: 229 self.heuristics[name] = Summary(name) 230 231 s = self.heuristics[name] 232 233 if prediction < 50: 234 hits = count - hits 235 remaining = count - hits 236 fits = max(hits, remaining) 237 238 s.edges.append(Heuristics(count, hits, fits)) 239 240 def add_loop_niter(self, niter): 241 if niter > 0: 242 self.niter_vector.append(niter) 243 244 def branches_max(self): 245 return max([v.branches() for k, v in self.heuristics.items()]) 246 247 def count_max(self): 248 return max([v.count() for k, v in self.heuristics.items()]) 249 250 def print_group(self, sorting, group_name, heuristics, predict_def): 251 count_max = self.count_max() 252 branches_max = self.branches_max() 253 254 sorter = lambda x: x.branches() 255 if sorting == 'branch-hitrate': 256 sorter = lambda x: x.get_branch_hitrate() 257 elif sorting == 'hitrate': 258 sorter = lambda x: x.get_hitrate() 259 elif sorting == 'coverage': 260 sorter = lambda x: x.count 261 elif sorting == 'name': 262 sorter = lambda x: x.name.lower() 263 264 print('%-40s %8s %6s %12s %18s %14s %8s %6s %12s %6s %s' % 265 ('HEURISTICS', 'BRANCHES', '(REL)', 266 'BR. HITRATE', 'HITRATE', 'COVERAGE', 'COVERAGE', '(REL)', 267 'predict.def', '(REL)', 'HOT branches (>%d%%)' % hot_threshold)) 268 for h in sorted(heuristics, key = sorter): 269 h.print(branches_max, count_max, predict_def) 270 271 def dump(self, sorting): 272 heuristics = self.heuristics.values() 273 if len(heuristics) == 0: 274 print('No heuristics available') 275 return 276 277 predict_def = None 278 if args.def_file != None: 279 predict_def = PredictDefFile(args.def_file) 280 predict_def.parse_and_modify(heuristics, args.write_def_file) 281 282 special = list(filter(lambda x: x.name in counter_aggregates, 283 heuristics)) 284 normal = list(filter(lambda x: x.name not in counter_aggregates, 285 heuristics)) 286 287 self.print_group(sorting, 'HEURISTICS', normal, predict_def) 288 print() 289 self.print_group(sorting, 'HEURISTIC AGGREGATES', special, predict_def) 290 291 if len(self.niter_vector) > 0: 292 print ('\nLoop count: %d' % len(self.niter_vector)), 293 print(' avg. # of iter: %.2f' % average(self.niter_vector)) 294 print(' median # of iter: %.2f' % median(self.niter_vector)) 295 for v in [1, 5, 10, 20, 30]: 296 cut = 0.01 * v 297 print(' avg. (%d%% cutoff) # of iter: %.2f' 298 % (v, average_cutoff(self.niter_vector, cut))) 299 300parser = argparse.ArgumentParser() 301parser.add_argument('dump_file', metavar = 'dump_file', 302 help = 'IPA profile dump file') 303parser.add_argument('-s', '--sorting', dest = 'sorting', 304 choices = ['branches', 'branch-hitrate', 'hitrate', 'coverage', 'name'], 305 default = 'branches') 306parser.add_argument('-d', '--def-file', help = 'path to predict.def') 307parser.add_argument('-w', '--write-def-file', action = 'store_true', 308 help = 'Modify predict.def file in order to set new numbers') 309parser.add_argument('-c', '--coverage-threshold', type = int, 310 help = 'Ignore edges that have percentage coverage >= coverage-threshold') 311parser.add_argument('-v', '--verbose', action = 'store_true', help = 'Print verbose informations') 312 313args = parser.parse_args() 314 315profile = Profile(args.dump_file) 316loop_niter_str = ';; profile-based iteration count: ' 317 318for l in open(args.dump_file): 319 if l.startswith(';;heuristics;'): 320 parts = l.strip().split(';') 321 assert len(parts) == 8 322 name = parts[3] 323 prediction = float(parts[6]) 324 count = int(parts[4]) 325 hits = int(parts[5]) 326 327 profile.add(name, prediction, count, hits) 328 elif l.startswith(loop_niter_str): 329 v = int(l[len(loop_niter_str):]) 330 profile.add_loop_niter(v) 331 332profile.dump(args.sorting) 333