analyze_brprob.py

#!/usr/bin/env python3

# Copyright (C) 2016-2025 Free Software Foundation, Inc.
#
# 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 i, l in enumerate(lines):
            if l.startswith('DEF_PREDICTOR'):
                next_line = lines[i + 1]
                if l.endswith(','):
                    l += next_line
                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)