Replace AWK script with the python script.

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

From-SVN: r235560

1 files changed, 136 insertions, 0 deletions

diff --git a/contrib/analyze_brprob.py b/contrib/analyze_brprob.py
new file mode 100644
index 0000000..36371ff
--- /dev/null
+++ b/contrib/analyze_brprob.py
@@ -0,0 +1,136 @@
+#!/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()