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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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74
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75 def percentage(a, b):
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76 return 100.0 * a / b
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77
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78 def average(values):
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79 return 1.0 * sum(values) / len(values)
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80
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81 def average_cutoff(values, cut):
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82 l = len(values)
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83 skip = floor(l * cut / 2)
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84 if skip > 0:
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85 values.sort()
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86 values = values[skip:-skip]
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87 return average(values)
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88
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89 def median(values):
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90 values.sort()
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91 return values[int(len(values) / 2)]
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92
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93 class PredictDefFile:
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94 def __init__(self, path):
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95 self.path = path
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96 self.predictors = {}
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97
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98 def parse_and_modify(self, heuristics, write_def_file):
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99 lines = [x.rstrip() for x in open(self.path).readlines()]
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100
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101 p = None
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102 modified_lines = []
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103 for l in lines:
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104 if l.startswith('DEF_PREDICTOR'):
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105 m = re.match('.*"(.*)".*', l)
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106 p = m.group(1)
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107 elif l == '':
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108 p = None
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109
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110 if p != None:
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111 heuristic = [x for x in heuristics if x.name == p]
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112 heuristic = heuristic[0] if len(heuristic) == 1 else None
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113
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114 m = re.match('.*HITRATE \(([^)]*)\).*', l)
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115 if (m != None):
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116 self.predictors[p] = int(m.group(1))
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117
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118 # modify the line
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119 if heuristic != None:
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120 new_line = (l[:m.start(1)]
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121 + str(round(heuristic.get_hitrate()))
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122 + l[m.end(1):])
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123 l = new_line
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124 p = None
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125 elif 'PROB_VERY_LIKELY' in l:
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126 self.predictors[p] = 100
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127 modified_lines.append(l)
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128
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129 # save the file
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130 if write_def_file:
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131 with open(self.path, 'w+') as f:
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132 for l in modified_lines:
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133 f.write(l + '\n')
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134
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135 class Summary:
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136 def __init__(self, name):
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137 self.name = name
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138 self.branches = 0
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139 self.successfull_branches = 0
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140 self.count = 0
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141 self.hits = 0
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142 self.fits = 0
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143
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144 def get_hitrate(self):
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145 return 100.0 * self.hits / self.count
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146
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147 def get_branch_hitrate(self):
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148 return 100.0 * self.successfull_branches / self.branches
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149
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150 def count_formatted(self):
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151 v = self.count
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152 for unit in ['','K','M','G','T','P','E','Z']:
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153 if v < 1000:
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154 return "%3.2f%s" % (v, unit)
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155 v /= 1000.0
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156 return "%.1f%s" % (v, 'Y')
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157
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158 def print(self, branches_max, count_max, predict_def):
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159 predicted_as = None
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160 if predict_def != None and self.name in predict_def.predictors:
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161 predicted_as = predict_def.predictors[self.name]
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162
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163 print('%-40s %8i %5.1f%% %11.2f%% %7.2f%% / %6.2f%% %14i %8s %5.1f%%' %
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164 (self.name, self.branches,
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165 percentage(self.branches, branches_max),
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166 self.get_branch_hitrate(),
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167 self.get_hitrate(),
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168 percentage(self.fits, self.count),
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169 self.count, self.count_formatted(),
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170 percentage(self.count, count_max)), end = '')
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171
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172 if predicted_as != None:
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173 print('%12i%% %5.1f%%' % (predicted_as,
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174 self.get_hitrate() - predicted_as), end = '')
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175 print()
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176
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177 class Profile:
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178 def __init__(self, filename):
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179 self.filename = filename
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180 self.heuristics = {}
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181 self.niter_vector = []
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182
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183 def add(self, name, prediction, count, hits):
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184 if not name in self.heuristics:
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185 self.heuristics[name] = Summary(name)
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186
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187 s = self.heuristics[name]
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188 s.branches += 1
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189
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190 s.count += count
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191 if prediction < 50:
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192 hits = count - hits
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193 remaining = count - hits
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194 if hits >= remaining:
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195 s.successfull_branches += 1
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196
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197 s.hits += hits
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198 s.fits += max(hits, remaining)
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199
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200 def add_loop_niter(self, niter):
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201 if niter > 0:
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202 self.niter_vector.append(niter)
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203
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204 def branches_max(self):
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205 return max([v.branches for k, v in self.heuristics.items()])
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206
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207 def count_max(self):
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208 return max([v.count for k, v in self.heuristics.items()])
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209
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210 def print_group(self, sorting, group_name, heuristics, predict_def):
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211 count_max = self.count_max()
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212 branches_max = self.branches_max()
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213
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214 sorter = lambda x: x.branches
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215 if sorting == 'branch-hitrate':
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216 sorter = lambda x: x.get_branch_hitrate()
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217 elif sorting == 'hitrate':
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218 sorter = lambda x: x.get_hitrate()
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219 elif sorting == 'coverage':
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220 sorter = lambda x: x.count
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221 elif sorting == 'name':
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222 sorter = lambda x: x.name.lower()
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223
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224 print('%-40s %8s %6s %12s %18s %14s %8s %6s %12s %6s' %
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225 ('HEURISTICS', 'BRANCHES', '(REL)',
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226 'BR. HITRATE', 'HITRATE', 'COVERAGE', 'COVERAGE', '(REL)',
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227 'predict.def', '(REL)'))
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228 for h in sorted(heuristics, key = sorter):
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229 h.print(branches_max, count_max, predict_def)
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230
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231 def dump(self, sorting):
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232 heuristics = self.heuristics.values()
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233 if len(heuristics) == 0:
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234 print('No heuristics available')
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235 return
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236
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237 predict_def = None
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238 if args.def_file != None:
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239 predict_def = PredictDefFile(args.def_file)
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240 predict_def.parse_and_modify(heuristics, args.write_def_file)
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241
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242 special = list(filter(lambda x: x.name in counter_aggregates,
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243 heuristics))
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244 normal = list(filter(lambda x: x.name not in counter_aggregates,
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245 heuristics))
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246
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247 self.print_group(sorting, 'HEURISTICS', normal, predict_def)
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248 print()
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249 self.print_group(sorting, 'HEURISTIC AGGREGATES', special, predict_def)
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250
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251 if len(self.niter_vector) > 0:
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252 print ('\nLoop count: %d' % len(self.niter_vector)),
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253 print(' avg. # of iter: %.2f' % average(self.niter_vector))
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254 print(' median # of iter: %.2f' % median(self.niter_vector))
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255 for v in [1, 5, 10, 20, 30]:
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256 cut = 0.01 * v
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257 print(' avg. (%d%% cutoff) # of iter: %.2f'
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258 % (v, average_cutoff(self.niter_vector, cut)))
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259
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260 parser = argparse.ArgumentParser()
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261 parser.add_argument('dump_file', metavar = 'dump_file',
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262 help = 'IPA profile dump file')
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263 parser.add_argument('-s', '--sorting', dest = 'sorting',
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264 choices = ['branches', 'branch-hitrate', 'hitrate', 'coverage', 'name'],
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265 default = 'branches')
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266 parser.add_argument('-d', '--def-file', help = 'path to predict.def')
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267 parser.add_argument('-w', '--write-def-file', action = 'store_true',
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268 help = 'Modify predict.def file in order to set new numbers')
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269
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270 args = parser.parse_args()
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271
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272 profile = Profile(args.dump_file)
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273 r = re.compile(' (.*) heuristics( of edge [0-9]*->[0-9]*)?( \\(.*\\))?: (.*)%.*exec ([0-9]*) hit ([0-9]*)')
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274 loop_niter_str = ';; profile-based iteration count: '
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275 for l in open(args.dump_file):
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276 m = r.match(l)
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277 if m != None and m.group(3) == None:
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278 name = m.group(1)
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279 prediction = float(m.group(4))
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280 count = int(m.group(5))
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281 hits = int(m.group(6))
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282
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283 profile.add(name, prediction, count, hits)
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284 elif l.startswith(loop_niter_str):
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285 v = int(l[len(loop_niter_str):])
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286 profile.add_loop_niter(v)
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287
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288 profile.dump(args.sorting)
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