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gcc/contrib/analyze_brprob.py
Martin Liska d1b9a5724b Fix usage of analyze_brprob.py script. 
2018-01-19  Martin Liska  <mliska@suse.cz>

	* analyze_brprob.py: Support new format that can be easily
	parsed. Add new column to report.
2018-01-19  Martin Liska  <mliska@suse.cz>

	* predict.c (dump_prediction): Add new format for
	analyze_brprob.py script which is enabled with -details
	suboption.
	* profile-count.h (precise_p): New function.

From-SVN: r256886
2018-01-19 12:03:24 +00:00

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#!/usr/bin/env python3
#
# Script to analyze results of our branch prediction heuristics
#
# This file is part of GCC.
#
# GCC is free software; you can redistribute it and/or modify it under
# the terms of the GNU General Public License as published by the Free
# Software Foundation; either version 3, or (at your option) any later
# version.
#
# GCC is distributed in the hope that it will be useful, but WITHOUT ANY
# WARRANTY; without even the implied warranty of MERCHANTABILITY or
# FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License
# for more details.
#
# You should have received a copy of the GNU General Public License
# along with GCC; see the file COPYING3. If not see
# <http://www.gnu.org/licenses/>. */
#
#
#
# This script is used to calculate two basic properties of the branch prediction
# heuristics - coverage and hitrate. Coverage is number of executions
# of a given branch matched by the heuristics and hitrate is probability
# that once branch is predicted as taken it is really taken.
#
# These values are useful to determine the quality of given heuristics.
# Hitrate may be directly used in predict.def.
#
# Usage:
# Step 1: Compile and profile your program. You need to use -fprofile-generate
# flag to get the profiles.
# Step 2: Make a reference run of the intrumented application.
# Step 3: Compile the program with collected profile and dump IPA profiles
# (-fprofile-use -fdump-ipa-profile-details)
# Step 4: Collect all generated dump files:
# find . -name '*.profile' | xargs cat > dump_file
# Step 5: Run the script:
# ./analyze_brprob.py dump_file
# and read results. Basically the following table is printed:
#
# HEURISTICS BRANCHES (REL) HITRATE COVERAGE (REL)
# early return (on trees) 3 0.2% 35.83% / 93.64% 66360 0.0%
# guess loop iv compare 8 0.6% 53.35% / 53.73% 11183344 0.0%
# call 18 1.4% 31.95% / 69.95% 51880179 0.2%
# loop guard 23 1.8% 84.13% / 84.85% 13749065956 42.2%
# opcode values positive (on trees) 42 3.3% 15.71% / 84.81% 6771097902 20.8%
# opcode values nonequal (on trees) 226 17.6% 72.48% / 72.84% 844753864 2.6%
# loop exit 231 18.0% 86.97% / 86.98% 8952666897 27.5%
# loop iterations 239 18.6% 91.10% / 91.10% 3062707264 9.4%
# DS theory 281 21.9% 82.08% / 83.39% 7787264075 23.9%
# no prediction 293 22.9% 46.92% / 70.70% 2293267840 7.0%
# guessed loop iterations 313 24.4% 76.41% / 76.41% 10782750177 33.1%
# first match 708 55.2% 82.30% / 82.31% 22489588691 69.0%
# combined 1282 100.0% 79.76% / 81.75% 32570120606 100.0%
#
#
# The heuristics called "first match" is a heuristics used by GCC branch
# prediction pass and it predicts 55.2% branches correctly. As you can,
# the heuristics has very good covertage (69.05%). On the other hand,
# "opcode values nonequal (on trees)" heuristics has good hirate, but poor
# coverage.
import sys
import os
import re
import argparse
from math import *
counter_aggregates = set(['combined', 'first match', 'DS theory',
'no prediction'])
hot_threshold = 10
def percentage(a, b):
return 100.0 * a / b
def average(values):
return 1.0 * sum(values) / len(values)
def average_cutoff(values, cut):
l = len(values)
skip = floor(l * cut / 2)
if skip > 0:
values.sort()
values = values[skip:-skip]
return average(values)
def median(values):
values.sort()
return values[int(len(values) / 2)]
class PredictDefFile:
def __init__(self, path):
self.path = path
self.predictors = {}
def parse_and_modify(self, heuristics, write_def_file):
lines = [x.rstrip() for x in open(self.path).readlines()]
p = None
modified_lines = []
for l in lines:
if l.startswith('DEF_PREDICTOR'):
m = re.match('.*"(.*)".*', l)
p = m.group(1)
elif l == '':
p = None
if p != None:
heuristic = [x for x in heuristics if x.name == p]
heuristic = heuristic[0] if len(heuristic) == 1 else None
m = re.match('.*HITRATE \(([^)]*)\).*', l)
if (m != None):
self.predictors[p] = int(m.group(1))
# modify the line
if heuristic != None:
new_line = (l[:m.start(1)]
+ str(round(heuristic.get_hitrate()))
+ l[m.end(1):])
l = new_line
p = None
elif 'PROB_VERY_LIKELY' in l:
self.predictors[p] = 100
modified_lines.append(l)
# save the file
if write_def_file:
with open(self.path, 'w+') as f:
for l in modified_lines:
f.write(l + '\n')
class Heuristics:
def __init__(self, count, hits, fits):
self.count = count
self.hits = hits
self.fits = fits
class Summary:
def __init__(self, name):
self.name = name
self.edges= []
def branches(self):
return len(self.edges)
def hits(self):
return sum([x.hits for x in self.edges])
def fits(self):
return sum([x.fits for x in self.edges])
def count(self):
return sum([x.count for x in self.edges])
def successfull_branches(self):
return len([x for x in self.edges if 2 * x.hits >= x.count])
def get_hitrate(self):
return 100.0 * self.hits() / self.count()
def get_branch_hitrate(self):
return 100.0 * self.successfull_branches() / self.branches()
def count_formatted(self):
v = self.count()
for unit in ['', 'k', 'M', 'G', 'T', 'P', 'E', 'Z', 'Y']:
if v < 1000:
return "%3.2f%s" % (v, unit)
v /= 1000.0
return "%.1f%s" % (v, 'Y')
def count(self):
return sum([x.count for x in self.edges])
def print(self, branches_max, count_max, predict_def):
# filter out most hot edges (if requested)
self.edges = sorted(self.edges, reverse = True, key = lambda x: x.count)
if args.coverage_threshold != None:
threshold = args.coverage_threshold * self.count() / 100
edges = [x for x in self.edges if x.count < threshold]
if len(edges) != 0:
self.edges = edges
predicted_as = None
if predict_def != None and self.name in predict_def.predictors:
predicted_as = predict_def.predictors[self.name]
print('%-40s %8i %5.1f%% %11.2f%% %7.2f%% / %6.2f%% %14i %8s %5.1f%%' %
(self.name, self.branches(),
percentage(self.branches(), branches_max),
self.get_branch_hitrate(),
self.get_hitrate(),
percentage(self.fits(), self.count()),
self.count(), self.count_formatted(),
percentage(self.count(), count_max)), end = '')
if predicted_as != None:
print('%12i%% %5.1f%%' % (predicted_as,
self.get_hitrate() - predicted_as), end = '')
else:
print(' ' * 20, end = '')
# print details about the most important edges
if args.coverage_threshold == None:
edges = [x for x in self.edges[:100] if x.count * hot_threshold > self.count()]
if args.verbose:
for c in edges:
r = 100.0 * c.count / self.count()
print(' %.0f%%:%d' % (r, c.count), end = '')
elif len(edges) > 0:
print(' %0.0f%%:%d' % (100.0 * sum([x.count for x in edges]) / self.count(), len(edges)), end = '')
print()
class Profile:
def __init__(self, filename):
self.filename = filename
self.heuristics = {}
self.niter_vector = []
def add(self, name, prediction, count, hits):
if not name in self.heuristics:
self.heuristics[name] = Summary(name)
s = self.heuristics[name]
if prediction < 50:
hits = count - hits
remaining = count - hits
fits = max(hits, remaining)
s.edges.append(Heuristics(count, hits, fits))
def add_loop_niter(self, niter):
if niter > 0:
self.niter_vector.append(niter)
def branches_max(self):
return max([v.branches() for k, v in self.heuristics.items()])
def count_max(self):
return max([v.count() for k, v in self.heuristics.items()])
def print_group(self, sorting, group_name, heuristics, predict_def):
count_max = self.count_max()
branches_max = self.branches_max()
sorter = lambda x: x.branches()
if sorting == 'branch-hitrate':
sorter = lambda x: x.get_branch_hitrate()
elif sorting == 'hitrate':
sorter = lambda x: x.get_hitrate()
elif sorting == 'coverage':
sorter = lambda x: x.count
elif sorting == 'name':
sorter = lambda x: x.name.lower()
print('%-40s %8s %6s %12s %18s %14s %8s %6s %12s %6s %s' %
('HEURISTICS', 'BRANCHES', '(REL)',
'BR. HITRATE', 'HITRATE', 'COVERAGE', 'COVERAGE', '(REL)',
'predict.def', '(REL)', 'HOT branches (>%d%%)' % hot_threshold))
for h in sorted(heuristics, key = sorter):
h.print(branches_max, count_max, predict_def)
def dump(self, sorting):
heuristics = self.heuristics.values()
if len(heuristics) == 0:
print('No heuristics available')
return
predict_def = None
if args.def_file != None:
predict_def = PredictDefFile(args.def_file)
predict_def.parse_and_modify(heuristics, args.write_def_file)
special = list(filter(lambda x: x.name in counter_aggregates,
heuristics))
normal = list(filter(lambda x: x.name not in counter_aggregates,
heuristics))
self.print_group(sorting, 'HEURISTICS', normal, predict_def)
print()
self.print_group(sorting, 'HEURISTIC AGGREGATES', special, predict_def)
if len(self.niter_vector) > 0:
print ('\nLoop count: %d' % len(self.niter_vector)),
print(' avg. # of iter: %.2f' % average(self.niter_vector))
print(' median # of iter: %.2f' % median(self.niter_vector))
for v in [1, 5, 10, 20, 30]:
cut = 0.01 * v
print(' avg. (%d%% cutoff) # of iter: %.2f'
% (v, average_cutoff(self.niter_vector, cut)))
parser = argparse.ArgumentParser()
parser.add_argument('dump_file', metavar = 'dump_file',
help = 'IPA profile dump file')
parser.add_argument('-s', '--sorting', dest = 'sorting',
choices = ['branches', 'branch-hitrate', 'hitrate', 'coverage', 'name'],
default = 'branches')
parser.add_argument('-d', '--def-file', help = 'path to predict.def')
parser.add_argument('-w', '--write-def-file', action = 'store_true',
help = 'Modify predict.def file in order to set new numbers')
parser.add_argument('-c', '--coverage-threshold', type = int,
help = 'Ignore edges that have percentage coverage >= coverage-threshold')
parser.add_argument('-v', '--verbose', action = 'store_true', help = 'Print verbose informations')
args = parser.parse_args()
profile = Profile(args.dump_file)
loop_niter_str = ';; profile-based iteration count: '
for l in open(args.dump_file):
if l.startswith(';;heuristics;'):
parts = l.strip().split(';')
assert len(parts) == 8
name = parts[3]
prediction = float(parts[6])
count = int(parts[4])
hits = int(parts[5])
profile.add(name, prediction, count, hits)
elif l.startswith(loop_niter_str):
v = int(l[len(loop_niter_str):])
profile.add_loop_niter(v)
profile.dump(args.sorting)
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