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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 *
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 Summary:
def __init__(self, name):
self.name = name
self.branches = 0
self.count = 0
self.hits = 0
self.fits = 0
def get_hitrate(self):
return self.hits / self.count
def count_formatted(self):
v = self.count
for unit in ['','K','M','G','T','P','E','Z']:
if v < 1000:
return "%3.2f%s" % (v, unit)
v /= 1000.0
return "%.1f%s" % (v, 'Y')
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]
s.branches += 1
s.count += count
if prediction < 50:
hits = count - hits
s.hits += hits
s.fits += max(hits, count - hits)
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 dump(self, sorting):
sorter = lambda x: x[1].branches
if sorting == 'hitrate':
sorter = lambda x: x[1].get_hitrate()
elif sorting == 'coverage':
sorter = lambda x: x[1].count
print('%-40s %8s %6s %-16s %14s %8s %6s' % ('HEURISTICS', 'BRANCHES', '(REL)',
'HITRATE', 'COVERAGE', 'COVERAGE', '(REL)'))
for (k, v) in sorted(self.heuristics.items(), key = sorter):
print('%-40s %8i %5.1f%% %6.2f%% / %6.2f%% %14i %8s %5.1f%%' %
(k, v.branches, percentage(v.branches, self.branches_max ()),
percentage(v.hits, v.count), percentage(v.fits, v.count),
v.count, v.count_formatted(), percentage(v.count, self.count_max()) ))
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', 'hitrate', 'coverage'], default = 'branches')
args = parser.parse_args()
profile = Profile(sys.argv[1])
r = re.compile(' (.*) heuristics( of edge [0-9]*->[0-9]*)?( \\(.*\\))?: (.*)%.*exec ([0-9]*) hit ([0-9]*)')
loop_niter_str = ';; profile-based iteration count: '
for l in open(args.dump_file).readlines():
m = r.match(l)
if m != None and m.group(3) == None:
name = m.group(1)
prediction = float(m.group(4))
count = int(m.group(5))
hits = int(m.group(6))
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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