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1 #!/usr/bin/env python3
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2 #
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3 # Script to analyze results of our branch prediction heuristics
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4 #
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5 # This file is part of GCC.
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6 #
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7 # GCC is free software; you can redistribute it and/or modify it under
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8 # the terms of the GNU General Public License as published by the Free
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9 # Software Foundation; either version 3, or (at your option) any later
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10 # version.
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11 #
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12 # GCC is distributed in the hope that it will be useful, but WITHOUT ANY
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13 # WARRANTY; without even the implied warranty of MERCHANTABILITY or
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14 # FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License
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15 # for more details.
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16 #
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17 # You should have received a copy of the GNU General Public License
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18 # along with GCC; see the file COPYING3. If not see
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19 # <http://www.gnu.org/licenses/>. */
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20 #
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21 #
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22 #
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23 # This script is used to calculate two basic properties of the branch prediction
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24 # heuristics - coverage and hitrate. Coverage is number of executions
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25 # of a given branch matched by the heuristics and hitrate is probability
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26 # that once branch is predicted as taken it is really taken.
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27 #
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28 # These values are useful to determine the quality of given heuristics.
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29 # Hitrate may be directly used in predict.def.
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30 #
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31 # Usage:
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32 # Step 1: Compile and profile your program. You need to use -fprofile-generate
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33 # flag to get the profiles.
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34 # Step 2: Make a reference run of the intrumented application.
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35 # Step 3: Compile the program with collected profile and dump IPA profiles
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36 # (-fprofile-use -fdump-ipa-profile-details)
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37 # Step 4: Collect all generated dump files:
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38 # find . -name '*.profile' | xargs cat > dump_file
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39 # Step 5: Run the script:
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40 # ./analyze_brprob.py dump_file
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41 # and read results. Basically the following table is printed:
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42 #
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43 # HEURISTICS BRANCHES (REL) HITRATE COVERAGE (REL)
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44 # early return (on trees) 3 0.2% 35.83% / 93.64% 66360 0.0%
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45 # guess loop iv compare 8 0.6% 53.35% / 53.73% 11183344 0.0%
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46 # call 18 1.4% 31.95% / 69.95% 51880179 0.2%
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47 # loop guard 23 1.8% 84.13% / 84.85% 13749065956 42.2%
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48 # opcode values positive (on trees) 42 3.3% 15.71% / 84.81% 6771097902 20.8%
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49 # opcode values nonequal (on trees) 226 17.6% 72.48% / 72.84% 844753864 2.6%
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50 # loop exit 231 18.0% 86.97% / 86.98% 8952666897 27.5%
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51 # loop iterations 239 18.6% 91.10% / 91.10% 3062707264 9.4%
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52 # DS theory 281 21.9% 82.08% / 83.39% 7787264075 23.9%
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53 # no prediction 293 22.9% 46.92% / 70.70% 2293267840 7.0%
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54 # guessed loop iterations 313 24.4% 76.41% / 76.41% 10782750177 33.1%
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55 # first match 708 55.2% 82.30% / 82.31% 22489588691 69.0%
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56 # combined 1282 100.0% 79.76% / 81.75% 32570120606 100.0%
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57 #
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58 #
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59 # The heuristics called "first match" is a heuristics used by GCC branch
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60 # prediction pass and it predicts 55.2% branches correctly. As you can,
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61 # the heuristics has very good covertage (69.05%). On the other hand,
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62 # "opcode values nonequal (on trees)" heuristics has good hirate, but poor
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63 # coverage.
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64
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65 import sys
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66 import os
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67 import re
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68 import argparse
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69
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70 from math import *
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71
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72 counter_aggregates = set(['combined', 'first match', 'DS theory',
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73 'no prediction'])
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131
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74 hot_threshold = 10
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111
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75
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76 def percentage(a, b):
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77 return 100.0 * a / b
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78
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79 def average(values):
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80 return 1.0 * sum(values) / len(values)
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81
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82 def average_cutoff(values, cut):
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83 l = len(values)
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84 skip = floor(l * cut / 2)
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85 if skip > 0:
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86 values.sort()
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87 values = values[skip:-skip]
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88 return average(values)
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89
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90 def median(values):
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91 values.sort()
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92 return values[int(len(values) / 2)]
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93
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94 class PredictDefFile:
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95 def __init__(self, path):
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96 self.path = path
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97 self.predictors = {}
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98
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99 def parse_and_modify(self, heuristics, write_def_file):
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100 lines = [x.rstrip() for x in open(self.path).readlines()]
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101
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102 p = None
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103 modified_lines = []
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104 for l in lines:
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105 if l.startswith('DEF_PREDICTOR'):
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106 m = re.match('.*"(.*)".*', l)
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107 p = m.group(1)
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108 elif l == '':
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109 p = None
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110
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111 if p != None:
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112 heuristic = [x for x in heuristics if x.name == p]
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113 heuristic = heuristic[0] if len(heuristic) == 1 else None
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114
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115 m = re.match('.*HITRATE \(([^)]*)\).*', l)
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116 if (m != None):
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117 self.predictors[p] = int(m.group(1))
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118
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119 # modify the line
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120 if heuristic != None:
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121 new_line = (l[:m.start(1)]
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122 + str(round(heuristic.get_hitrate()))
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123 + l[m.end(1):])
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124 l = new_line
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125 p = None
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126 elif 'PROB_VERY_LIKELY' in l:
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127 self.predictors[p] = 100
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128 modified_lines.append(l)
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129
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130 # save the file
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131 if write_def_file:
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132 with open(self.path, 'w+') as f:
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133 for l in modified_lines:
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134 f.write(l + '\n')
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131
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135 class Heuristics:
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136 def __init__(self, count, hits, fits):
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137 self.count = count
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138 self.hits = hits
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139 self.fits = fits
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111
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140
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141 class Summary:
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142 def __init__(self, name):
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143 self.name = name
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131
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144 self.edges= []
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145
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146 def branches(self):
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147 return len(self.edges)
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148
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149 def hits(self):
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150 return sum([x.hits for x in self.edges])
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151
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152 def fits(self):
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153 return sum([x.fits for x in self.edges])
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154
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155 def count(self):
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156 return sum([x.count for x in self.edges])
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157
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158 def successfull_branches(self):
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159 return len([x for x in self.edges if 2 * x.hits >= x.count])
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111
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160
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161 def get_hitrate(self):
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131
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162 return 100.0 * self.hits() / self.count()
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111
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163
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164 def get_branch_hitrate(self):
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131
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165 return 100.0 * self.successfull_branches() / self.branches()
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111
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166
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167 def count_formatted(self):
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131
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168 v = self.count()
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169 for unit in ['', 'k', 'M', 'G', 'T', 'P', 'E', 'Z', 'Y']:
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111
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170 if v < 1000:
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171 return "%3.2f%s" % (v, unit)
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172 v /= 1000.0
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173 return "%.1f%s" % (v, 'Y')
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174
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131
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175 def count(self):
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176 return sum([x.count for x in self.edges])
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177
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111
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178 def print(self, branches_max, count_max, predict_def):
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131
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179 # filter out most hot edges (if requested)
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180 self.edges = sorted(self.edges, reverse = True, key = lambda x: x.count)
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181 if args.coverage_threshold != None:
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182 threshold = args.coverage_threshold * self.count() / 100
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183 edges = [x for x in self.edges if x.count < threshold]
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184 if len(edges) != 0:
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185 self.edges = edges
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186
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111
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187 predicted_as = None
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188 if predict_def != None and self.name in predict_def.predictors:
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189 predicted_as = predict_def.predictors[self.name]
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190
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191 print('%-40s %8i %5.1f%% %11.2f%% %7.2f%% / %6.2f%% %14i %8s %5.1f%%' %
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131
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192 (self.name, self.branches(),
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193 percentage(self.branches(), branches_max),
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194 self.get_branch_hitrate(),
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195 self.get_hitrate(),
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131
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196 percentage(self.fits(), self.count()),
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197 self.count(), self.count_formatted(),
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198 percentage(self.count(), count_max)), end = '')
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111
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199
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200 if predicted_as != None:
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201 print('%12i%% %5.1f%%' % (predicted_as,
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202 self.get_hitrate() - predicted_as), end = '')
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131
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203 else:
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204 print(' ' * 20, end = '')
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205
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206 # print details about the most important edges
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207 if args.coverage_threshold == None:
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208 edges = [x for x in self.edges[:100] if x.count * hot_threshold > self.count()]
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209 if args.verbose:
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210 for c in edges:
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211 r = 100.0 * c.count / self.count()
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212 print(' %.0f%%:%d' % (r, c.count), end = '')
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213 elif len(edges) > 0:
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214 print(' %0.0f%%:%d' % (100.0 * sum([x.count for x in edges]) / self.count(), len(edges)), end = '')
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215
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111
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216 print()
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217
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218 class Profile:
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219 def __init__(self, filename):
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220 self.filename = filename
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221 self.heuristics = {}
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222 self.niter_vector = []
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223
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224 def add(self, name, prediction, count, hits):
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225 if not name in self.heuristics:
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226 self.heuristics[name] = Summary(name)
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227
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228 s = self.heuristics[name]
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229
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230 if prediction < 50:
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231 hits = count - hits
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232 remaining = count - hits
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131
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233 fits = max(hits, remaining)
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234
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131
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235 s.edges.append(Heuristics(count, hits, fits))
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236
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237 def add_loop_niter(self, niter):
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238 if niter > 0:
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239 self.niter_vector.append(niter)
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240
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241 def branches_max(self):
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131
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242 return max([v.branches() for k, v in self.heuristics.items()])
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111
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243
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244 def count_max(self):
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131
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245 return max([v.count() for k, v in self.heuristics.items()])
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111
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246
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247 def print_group(self, sorting, group_name, heuristics, predict_def):
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248 count_max = self.count_max()
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249 branches_max = self.branches_max()
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250
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131
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251 sorter = lambda x: x.branches()
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111
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252 if sorting == 'branch-hitrate':
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253 sorter = lambda x: x.get_branch_hitrate()
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254 elif sorting == 'hitrate':
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255 sorter = lambda x: x.get_hitrate()
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256 elif sorting == 'coverage':
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257 sorter = lambda x: x.count
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258 elif sorting == 'name':
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259 sorter = lambda x: x.name.lower()
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260
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131
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261 print('%-40s %8s %6s %12s %18s %14s %8s %6s %12s %6s %s' %
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262 ('HEURISTICS', 'BRANCHES', '(REL)',
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263 'BR. HITRATE', 'HITRATE', 'COVERAGE', 'COVERAGE', '(REL)',
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131
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264 'predict.def', '(REL)', 'HOT branches (>%d%%)' % hot_threshold))
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265 for h in sorted(heuristics, key = sorter):
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266 h.print(branches_max, count_max, predict_def)
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267
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268 def dump(self, sorting):
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269 heuristics = self.heuristics.values()
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270 if len(heuristics) == 0:
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271 print('No heuristics available')
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272 return
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273
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274 predict_def = None
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275 if args.def_file != None:
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276 predict_def = PredictDefFile(args.def_file)
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277 predict_def.parse_and_modify(heuristics, args.write_def_file)
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278
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279 special = list(filter(lambda x: x.name in counter_aggregates,
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280 heuristics))
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281 normal = list(filter(lambda x: x.name not in counter_aggregates,
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282 heuristics))
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283
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284 self.print_group(sorting, 'HEURISTICS', normal, predict_def)
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285 print()
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286 self.print_group(sorting, 'HEURISTIC AGGREGATES', special, predict_def)
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287
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288 if len(self.niter_vector) > 0:
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289 print ('\nLoop count: %d' % len(self.niter_vector)),
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290 print(' avg. # of iter: %.2f' % average(self.niter_vector))
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291 print(' median # of iter: %.2f' % median(self.niter_vector))
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292 for v in [1, 5, 10, 20, 30]:
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293 cut = 0.01 * v
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294 print(' avg. (%d%% cutoff) # of iter: %.2f'
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295 % (v, average_cutoff(self.niter_vector, cut)))
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296
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297 parser = argparse.ArgumentParser()
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298 parser.add_argument('dump_file', metavar = 'dump_file',
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299 help = 'IPA profile dump file')
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300 parser.add_argument('-s', '--sorting', dest = 'sorting',
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301 choices = ['branches', 'branch-hitrate', 'hitrate', 'coverage', 'name'],
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302 default = 'branches')
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303 parser.add_argument('-d', '--def-file', help = 'path to predict.def')
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304 parser.add_argument('-w', '--write-def-file', action = 'store_true',
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305 help = 'Modify predict.def file in order to set new numbers')
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131
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306 parser.add_argument('-c', '--coverage-threshold', type = int,
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307 help = 'Ignore edges that have percentage coverage >= coverage-threshold')
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308 parser.add_argument('-v', '--verbose', action = 'store_true', help = 'Print verbose informations')
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111
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309
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310 args = parser.parse_args()
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311
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312 profile = Profile(args.dump_file)
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313 loop_niter_str = ';; profile-based iteration count: '
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131
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314
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111
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315 for l in open(args.dump_file):
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131
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316 if l.startswith(';;heuristics;'):
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317 parts = l.strip().split(';')
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318 assert len(parts) == 8
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319 name = parts[3]
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320 prediction = float(parts[6])
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321 count = int(parts[4])
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322 hits = int(parts[5])
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111
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323
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324 profile.add(name, prediction, count, hits)
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325 elif l.startswith(loop_niter_str):
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326 v = int(l[len(loop_niter_str):])
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327 profile.add_loop_niter(v)
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328
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329 profile.dump(args.sorting)
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