Daily bump.
[official-gcc.git] / contrib / analyze_brprob.py
blobde5f474d6298c224cbd6000b601e5b18fb39650e
1 #!/usr/bin/env python3
3 # Script to analyze results of our branch prediction heuristics
5 # This file is part of GCC.
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.
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.
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/>. */
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.
28 # These values are useful to determine the quality of given heuristics.
29 # Hitrate may be directly used in predict.def.
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:
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%
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.
65 import sys
66 import os
67 import re
68 import argparse
70 from math import *
72 counter_aggregates = set(['combined', 'first match', 'DS theory',
73 'no prediction'])
74 hot_threshold = 10
76 def percentage(a, b):
77 return 100.0 * a / b
79 def average(values):
80 return 1.0 * sum(values) / len(values)
82 def 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)
90 def median(values):
91 values.sort()
92 return values[int(len(values) / 2)]
94 class PredictDefFile:
95 def __init__(self, path):
96 self.path = path
97 self.predictors = {}
99 def parse_and_modify(self, heuristics, write_def_file):
100 lines = [x.rstrip() for x in open(self.path).readlines()]
102 p = None
103 modified_lines = []
104 for l in lines:
105 if l.startswith('DEF_PREDICTOR'):
106 m = re.match('.*"(.*)".*', l)
107 p = m.group(1)
108 elif l == '':
109 p = None
111 if p != None:
112 heuristic = [x for x in heuristics if x.name == p]
113 heuristic = heuristic[0] if len(heuristic) == 1 else None
115 m = re.match('.*HITRATE \(([^)]*)\).*', l)
116 if (m != None):
117 self.predictors[p] = int(m.group(1))
119 # modify the line
120 if heuristic != None:
121 new_line = (l[:m.start(1)]
122 + str(round(heuristic.get_hitrate()))
123 + l[m.end(1):])
124 l = new_line
125 p = None
126 elif 'PROB_VERY_LIKELY' in l:
127 self.predictors[p] = 100
128 modified_lines.append(l)
130 # save the file
131 if write_def_file:
132 with open(self.path, 'w+') as f:
133 for l in modified_lines:
134 f.write(l + '\n')
135 class Heuristics:
136 def __init__(self, count, hits, fits):
137 self.count = count
138 self.hits = hits
139 self.fits = fits
141 class Summary:
142 def __init__(self, name):
143 self.name = name
144 self.edges= []
146 def branches(self):
147 return len(self.edges)
149 def hits(self):
150 return sum([x.hits for x in self.edges])
152 def fits(self):
153 return sum([x.fits for x in self.edges])
155 def count(self):
156 return sum([x.count for x in self.edges])
158 def successfull_branches(self):
159 return len([x for x in self.edges if 2 * x.hits >= x.count])
161 def get_hitrate(self):
162 return 100.0 * self.hits() / self.count()
164 def get_branch_hitrate(self):
165 return 100.0 * self.successfull_branches() / self.branches()
167 def count_formatted(self):
168 v = self.count()
169 for unit in ['', 'k', 'M', 'G', 'T', 'P', 'E', 'Z', 'Y']:
170 if v < 1000:
171 return "%3.2f%s" % (v, unit)
172 v /= 1000.0
173 return "%.1f%s" % (v, 'Y')
175 def count(self):
176 return sum([x.count for x in self.edges])
178 def print(self, branches_max, count_max, predict_def):
179 # filter out most hot edges (if requested)
180 self.edges = sorted(self.edges, reverse = True, key = lambda x: x.count)
181 if args.coverage_threshold != None:
182 threshold = args.coverage_threshold * self.count() / 100
183 edges = [x for x in self.edges if x.count < threshold]
184 if len(edges) != 0:
185 self.edges = edges
187 predicted_as = None
188 if predict_def != None and self.name in predict_def.predictors:
189 predicted_as = predict_def.predictors[self.name]
191 print('%-40s %8i %5.1f%% %11.2f%% %7.2f%% / %6.2f%% %14i %8s %5.1f%%' %
192 (self.name, self.branches(),
193 percentage(self.branches(), branches_max),
194 self.get_branch_hitrate(),
195 self.get_hitrate(),
196 percentage(self.fits(), self.count()),
197 self.count(), self.count_formatted(),
198 percentage(self.count(), count_max)), end = '')
200 if predicted_as != None:
201 print('%12i%% %5.1f%%' % (predicted_as,
202 self.get_hitrate() - predicted_as), end = '')
203 else:
204 print(' ' * 20, end = '')
206 # print details about the most important edges
207 if args.coverage_threshold == None:
208 edges = [x for x in self.edges[:100] if x.count * hot_threshold > self.count()]
209 if args.verbose:
210 for c in edges:
211 r = 100.0 * c.count / self.count()
212 print(' %.0f%%:%d' % (r, c.count), end = '')
213 elif len(edges) > 0:
214 print(' %0.0f%%:%d' % (100.0 * sum([x.count for x in edges]) / self.count(), len(edges)), end = '')
216 print()
218 class Profile:
219 def __init__(self, filename):
220 self.filename = filename
221 self.heuristics = {}
222 self.niter_vector = []
224 def add(self, name, prediction, count, hits):
225 if not name in self.heuristics:
226 self.heuristics[name] = Summary(name)
228 s = self.heuristics[name]
230 if prediction < 50:
231 hits = count - hits
232 remaining = count - hits
233 fits = max(hits, remaining)
235 s.edges.append(Heuristics(count, hits, fits))
237 def add_loop_niter(self, niter):
238 if niter > 0:
239 self.niter_vector.append(niter)
241 def branches_max(self):
242 return max([v.branches() for k, v in self.heuristics.items()])
244 def count_max(self):
245 return max([v.count() for k, v in self.heuristics.items()])
247 def print_group(self, sorting, group_name, heuristics, predict_def):
248 count_max = self.count_max()
249 branches_max = self.branches_max()
251 sorter = lambda x: x.branches()
252 if sorting == 'branch-hitrate':
253 sorter = lambda x: x.get_branch_hitrate()
254 elif sorting == 'hitrate':
255 sorter = lambda x: x.get_hitrate()
256 elif sorting == 'coverage':
257 sorter = lambda x: x.count
258 elif sorting == 'name':
259 sorter = lambda x: x.name.lower()
261 print('%-40s %8s %6s %12s %18s %14s %8s %6s %12s %6s %s' %
262 ('HEURISTICS', 'BRANCHES', '(REL)',
263 'BR. HITRATE', 'HITRATE', 'COVERAGE', 'COVERAGE', '(REL)',
264 'predict.def', '(REL)', 'HOT branches (>%d%%)' % hot_threshold))
265 for h in sorted(heuristics, key = sorter):
266 h.print(branches_max, count_max, predict_def)
268 def dump(self, sorting):
269 heuristics = self.heuristics.values()
270 if len(heuristics) == 0:
271 print('No heuristics available')
272 return
274 predict_def = None
275 if args.def_file != None:
276 predict_def = PredictDefFile(args.def_file)
277 predict_def.parse_and_modify(heuristics, args.write_def_file)
279 special = list(filter(lambda x: x.name in counter_aggregates,
280 heuristics))
281 normal = list(filter(lambda x: x.name not in counter_aggregates,
282 heuristics))
284 self.print_group(sorting, 'HEURISTICS', normal, predict_def)
285 print()
286 self.print_group(sorting, 'HEURISTIC AGGREGATES', special, predict_def)
288 if len(self.niter_vector) > 0:
289 print ('\nLoop count: %d' % len(self.niter_vector)),
290 print(' avg. # of iter: %.2f' % average(self.niter_vector))
291 print(' median # of iter: %.2f' % median(self.niter_vector))
292 for v in [1, 5, 10, 20, 30]:
293 cut = 0.01 * v
294 print(' avg. (%d%% cutoff) # of iter: %.2f'
295 % (v, average_cutoff(self.niter_vector, cut)))
297 parser = argparse.ArgumentParser()
298 parser.add_argument('dump_file', metavar = 'dump_file',
299 help = 'IPA profile dump file')
300 parser.add_argument('-s', '--sorting', dest = 'sorting',
301 choices = ['branches', 'branch-hitrate', 'hitrate', 'coverage', 'name'],
302 default = 'branches')
303 parser.add_argument('-d', '--def-file', help = 'path to predict.def')
304 parser.add_argument('-w', '--write-def-file', action = 'store_true',
305 help = 'Modify predict.def file in order to set new numbers')
306 parser.add_argument('-c', '--coverage-threshold', type = int,
307 help = 'Ignore edges that have percentage coverage >= coverage-threshold')
308 parser.add_argument('-v', '--verbose', action = 'store_true', help = 'Print verbose informations')
310 args = parser.parse_args()
312 profile = Profile(args.dump_file)
313 loop_niter_str = ';; profile-based iteration count: '
315 for l in open(args.dump_file):
316 if l.startswith(';;heuristics;'):
317 parts = l.strip().split(';')
318 assert len(parts) == 8
319 name = parts[3]
320 prediction = float(parts[6])
321 count = int(parts[4])
322 hits = int(parts[5])
324 profile.add(name, prediction, count, hits)
325 elif l.startswith(loop_niter_str):
326 v = int(l[len(loop_niter_str):])
327 profile.add_loop_niter(v)
329 profile.dump(args.sorting)