1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
|
//===-- Analyze benchmark JSON files --------------------------------------===//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
//
//===----------------------------------------------------------------------===//
// This code analyzes the json file produced by the `automemcpy` binary.
//
// As a remainder, `automemcpy` will benchmark each autogenerated memory
// functions against one of the predefined distributions available in the
// `libc/benchmarks/distributions` folder.
//
// It works as follows:
// - Reads one or more json files.
// - If there are several runs for the same function and distribution, picks the
// median throughput (aka `BytesPerSecond`).
// - Aggregates the throughput per distributions and scores them from worst (0)
// to best (1).
// - Each distribution categorizes each function into one of the following
// categories: EXCELLENT, VERY_GOOD, GOOD, PASSABLE, INADEQUATE, MEDIOCRE,
// BAD.
// - A process similar to the Majority Judgment voting system is used to `elect`
// the best function. The histogram of grades is returned so we can
// distinguish between functions with the same final grade. In the following
// example both functions grade EXCELLENT but we may prefer the second one.
//
// | | EXCELLENT | VERY_GOOD | GOOD | PASSABLE | ...
// |------------|-----------|-----------|------|----------| ...
// | Function_1 | 7 | 1 | 2 | | ...
// | Function_2 | 6 | 4 | | | ...
#include "automemcpy/ResultAnalyzer.h"
#include "llvm/ADT/StringRef.h"
#include <numeric>
#include <unordered_map>
namespace llvm {
namespace automemcpy {
StringRef Grade::getString(const GradeEnum &GE) {
switch (GE) {
case EXCELLENT:
return "EXCELLENT";
case VERY_GOOD:
return "VERY_GOOD";
case GOOD:
return "GOOD";
case PASSABLE:
return "PASSABLE";
case INADEQUATE:
return "INADEQUATE";
case MEDIOCRE:
return "MEDIOCRE";
case BAD:
return "BAD";
case ARRAY_SIZE:
report_fatal_error("logic error");
}
}
Grade::GradeEnum Grade::judge(double Score) {
if (Score >= 6. / 7)
return EXCELLENT;
if (Score >= 5. / 7)
return VERY_GOOD;
if (Score >= 4. / 7)
return GOOD;
if (Score >= 3. / 7)
return PASSABLE;
if (Score >= 2. / 7)
return INADEQUATE;
if (Score >= 1. / 7)
return MEDIOCRE;
return BAD;
}
static double computeUnbiasedSampleVariance(const std::vector<double> &Samples,
const double SampleMean) {
assert(!Samples.empty());
if (Samples.size() == 1)
return 0;
double DiffSquaresSum = 0;
for (const double S : Samples) {
const double Diff = S - SampleMean;
DiffSquaresSum += Diff * Diff;
}
return DiffSquaresSum / (Samples.size() - 1);
}
static void processPerDistributionData(PerDistributionData &Data) {
auto &Samples = Data.BytesPerSecondSamples;
assert(!Samples.empty());
// Sample Mean
const double Sum = std::accumulate(Samples.begin(), Samples.end(), 0.0);
Data.BytesPerSecondMean = Sum / Samples.size();
// Unbiased Sample Variance
Data.BytesPerSecondVariance =
computeUnbiasedSampleVariance(Samples, Data.BytesPerSecondMean);
// Median
const size_t HalfSize = Samples.size() / 2;
std::nth_element(Samples.begin(), Samples.begin() + HalfSize, Samples.end());
Data.BytesPerSecondMedian = Samples[HalfSize];
}
std::vector<FunctionData> getThroughputs(ArrayRef<Sample> Samples) {
std::unordered_map<FunctionId, FunctionData, FunctionId::Hasher> Functions;
for (const auto &S : Samples) {
if (S.Type != SampleType::ITERATION)
break;
auto &Function = Functions[S.Id.Function];
auto &Data = Function.PerDistributionData[S.Id.Distribution.Name];
Data.BytesPerSecondSamples.push_back(S.BytesPerSecond);
}
std::vector<FunctionData> Output;
for (auto &[FunctionId, Function] : Functions) {
Function.Id = FunctionId;
for (auto &Pair : Function.PerDistributionData)
processPerDistributionData(Pair.second);
Output.push_back(std::move(Function));
}
return Output;
}
void fillScores(MutableArrayRef<FunctionData> Functions) {
// A key to bucket throughput per function type and distribution.
struct Key {
FunctionType Type;
StringRef Distribution;
COMPARABLE_AND_HASHABLE(Key, Type, Distribution)
};
// Tracks minimum and maximum values.
struct MinMax {
double Min = std::numeric_limits<double>::max();
double Max = std::numeric_limits<double>::min();
void update(double Value) {
if (Value < Min)
Min = Value;
if (Value > Max)
Max = Value;
}
double normalize(double Value) const { return (Value - Min) / (Max - Min); }
};
std::unordered_map<Key, MinMax, Key::Hasher> ThroughputMinMax;
for (const auto &Function : Functions) {
const FunctionType Type = Function.Id.Type;
for (const auto &Pair : Function.PerDistributionData) {
const auto &Distribution = Pair.getKey();
const double Throughput = Pair.getValue().BytesPerSecondMedian;
const Key K{Type, Distribution};
ThroughputMinMax[K].update(Throughput);
}
}
for (auto &Function : Functions) {
const FunctionType Type = Function.Id.Type;
for (const auto &Pair : Function.PerDistributionData) {
const auto &Distribution = Pair.getKey();
const double Throughput = Pair.getValue().BytesPerSecondMedian;
const Key K{Type, Distribution};
Function.PerDistributionData[Distribution].Score =
ThroughputMinMax[K].normalize(Throughput);
}
}
}
void castVotes(MutableArrayRef<FunctionData> Functions) {
for (FunctionData &Function : Functions) {
Function.ScoresGeoMean = 1.0;
for (const auto &Pair : Function.PerDistributionData) {
const StringRef Distribution = Pair.getKey();
const double Score = Pair.getValue().Score;
Function.ScoresGeoMean *= Score;
const auto G = Grade::judge(Score);
++(Function.GradeHisto[G]);
Function.PerDistributionData[Distribution].Grade = G;
}
}
for (FunctionData &Function : Functions) {
const auto &GradeHisto = Function.GradeHisto;
const size_t Votes =
std::accumulate(GradeHisto.begin(), GradeHisto.end(), 0U);
const size_t MedianVote = Votes / 2;
size_t CountedVotes = 0;
Grade::GradeEnum MedianGrade = Grade::BAD;
for (size_t I = 0; I < GradeHisto.size(); ++I) {
CountedVotes += GradeHisto[I];
if (CountedVotes > MedianVote) {
MedianGrade = Grade::GradeEnum(I);
break;
}
}
Function.FinalGrade = MedianGrade;
}
}
} // namespace automemcpy
} // namespace llvm
|
