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Commit 4877829b by Martin Liska Committed by Martin Liska
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Replace AWK script with the python script. 

	* analyze_brprob: Remove.
	* analyze_brprob.py: New file.

From-SVN: r235560
parent 28633bbd
Showing with 141 additions and 147 deletions
contrib/ChangeLog
2016-04-28 Martin Liska <mliska@suse.cz>
* analyze_brprob: Remove.
* analyze_brprob.py: New file.
2016-04-27 Jakub Jelinek <jakub@redhat.com> 2016-04-27 Jakub Jelinek <jakub@redhat.com>
* gennews (files): Add files for GCC 6. * gennews (files): Add files for GCC 6.
......
contrib/analyze_brprob deleted 100755 → 0
#!/usr/bin/awk -f
# Script to analyze experimental results of our branch prediction heuristics
# Contributed by Jan Hubicka, SuSE Inc.
# Copyright (C) 2001, 2003 Free Software Foundation, Inc.
#
# 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 COPYING. If not, write to
# the Free Software Foundation, 51 Franklin Street, Fifth Floor,
# Boston, MA 02110-1301, USA.
#
#
# 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.c.
#
# Usage:
# Step 1: Compile and profile your program. You need to use -fprofile-arcs
# flag to get the profiles
# Step 2: Generate log files. The information about given heuristics are
# saved into ipa-profile dumps. You need to pass the -fdimp-ipa-profile switch
# to the compiler as well
# as -fbranch-probabilities to get the results of profiling noted in the dumps.
# Ensure that there are no "Arc profiling: some edge counts were bad." warnings.
# Step 3: Run this script to concatenate all *.profile files:
# analyze_brprob `find . -name *.profile`
# the information is collected and print once all files are parsed. This
# may take a while.
# Note that the script does use bc to perform long arithmetic.
# Step 4: Read the results. Basically the following table is printed:
# (this is just an example from a very early stage of branch prediction pass
# development, so please don't take these numbers seriously)
#
#HEURISTICS BRANCHES (REL) HITRATE COVERAGE (REL)
#opcode 2889 83.7% 94.96%/ 97.62% 7516383 75.3%
#pointer 246 7.1% 99.69%/ 99.86% 118791 1.2%
#loop header 449 13.0% 98.32%/ 99.07% 43553 0.4%
#first match 3450 100.0% 89.92%/ 97.27% 9979782 100.0%
#loop exit 924 26.8% 88.95%/ 95.58% 9026266 90.4%
#error return 150 4.3% 64.48%/ 86.81% 453542 4.5%
#call 803 23.3% 51.66%/ 98.61% 3614037 36.2%
#loop branch 51 1.5% 99.26%/ 99.27% 26854 0.3%
#noreturn call 951 27.6% 100.00%/100.00% 1759809 17.6%
#
# The heuristic called "first match" is a heuristic used by GCC branch
# prediction pass and it predicts 89.92% branches correctly.
#
# The quality of heuristics can be rated using both, coverage and hitrate
# parameters. For example "loop branch" heuristics (predicting loopback edge
# as taken) have both very high hitrate and coverage, so it is very useful.
# On the other hand, "exit block" heuristics (predicting exit edges as not
# taken) have good hitrate, but poor coverage, so only 3 branches have been
# predicted. The "loop header" heuristic has problems, since it tends to
# misspredict.
#
# The implementation of this script is somewhat brute force. My awk skills
# are limited.
function longeval(e)
{
e = "echo \"scale = 2 ;"e"\" | bc"
e | getline res
close (e)
return res
}
BEGIN {nnames = 0}
/^ .* heuristics: .*.$/ {
name=$0
sub (/^ /,"",name)
sub (/ heuristics: .*.$/,"",name)
if (!(name in branches))
{
names[nnames] = name
branches[name]=0
counts[name]=0
hits[name]=0
phits[name]=0
nnames++
}
branches[name]+=1
}
/^ .* heuristics: .*. exec [0-9]* hit [0-9]* (.*.)$/ {
name=$0
sub (/^ /,"",name)
sub (/ heuristics: .*. exec [0-9]* hit [0-9]* (.*.)$/,"",name)
pred=$0
sub (/^ .* heuristics: /,"",pred)
sub (/. exec [0-9]* hit [0-9]* (.*.)$/,"",pred)
count=$0
sub (/^ .* heuristics: .*. exec /,"",count)
sub (/ hit [0-9]* (.*.)$/,"",count)
hit=$0
sub (/^ .* heuristics: .*. exec [0-9]* hit /,"",hit)
sub (/ (.*.)$/,"",hit)
if (int(pred) < 50.0)
{
hit = count"-"hit;
}
counts[name]=counts[name] "+" count
hits[name]=hits[name] "+" hit
phits[name]=phits[name] "+(("hit")<"count"/2)*("count"-("hit"))+(("hit")>="count"/2)*("hit")"
#BC crashes on long strings. Irritating.
if (length(counts[name]) > 2000)
counts[name] = longeval(counts[name])
if (length(hits[name]) > 2000)
hits[name] = longeval(hits[name])
if (length(phits[name]) > 2000)
phits[name] = longeval(phits[name])
}
END {
# Heuristics called combined predicts just everything.
maxcounts = longeval(counts["combined"])
maxbranches = branches["combined"]
max = names["combined"]
printf("HEURISTICS BRANCHES (REL) HITRATE COVERAGE (REL)\n")
for (i = 0; i < nnames ; i++)
{
name = names[i]
counts[name] = longeval(counts[name])
printf ("%-26s %8i %5.1f%% %6s%% / %6s%% %12s %5.1f%%\n",
name,
branches[name], branches[name] * 100 / maxbranches,
longeval("("hits[name]") * 100 /(" counts[name]"-0.00001)"),
longeval("("phits[name]") * 100 /(" counts[name]"-0.00001)"),
counts[name], longeval(counts[name]" * 100 / ("maxcounts"-0.00001)"))
}
}
contrib/analyze_brprob.py 0 → 100644
#!/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
def percentage(a, b):
return 100.0 * a / b
class Summary:
def __init__(self, name):
self.name = name
self.branches = 0
self.count = 0
self.hits = 0
self.fits = 0
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 = {}
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 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):
print('%-36s %8s %6s %-16s %14s %8s %6s' % ('HEURISTICS', 'BRANCHES', '(REL)',
'HITRATE', 'COVERAGE', 'COVERAGE', '(REL)'))
for (k, v) in sorted(self.heuristics.items(), key = lambda x: x[1].branches):
print('%-36s %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(sys.argv) != 2:
print('Usage: ./analyze_brprob.py dump_file')
exit(1)
profile = Profile(sys.argv[1])
r = re.compile(' (.*) heuristics: (.*)%.*exec ([0-9]*) hit ([0-9]*)')
for l in open(profile.filename).readlines():
m = r.match(l)
if m != None:
name = m.group(1)
prediction = float(m.group(2))
count = int(m.group(3))
hits = int(m.group(4))
profile.add(name, prediction, count, hits)
profile.dump()
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