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
| //===-- Clustering.h --------------------------------------------*- C++ -*-===//
//
// 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
//
//===----------------------------------------------------------------------===//
///
/// \file
/// Utilities to compute benchmark result clusters.
///
//===----------------------------------------------------------------------===//
#ifndef LLVM_TOOLS_LLVM_EXEGESIS_CLUSTERING_H
#define LLVM_TOOLS_LLVM_EXEGESIS_CLUSTERING_H
#include "BenchmarkResult.h"
#include "llvm/ADT/Optional.h"
#include "llvm/Support/Error.h"
#include <limits>
#include <vector>
namespace llvm {
namespace exegesis {
class InstructionBenchmarkClustering {
public:
enum ModeE { Dbscan, Naive };
// Clusters `Points` using DBSCAN with the given parameters. See the cc file
// for more explanations on the algorithm.
static Expected<InstructionBenchmarkClustering>
create(const std::vector<InstructionBenchmark> &Points, ModeE Mode,
size_t DbscanMinPts, double AnalysisClusteringEpsilon,
Optional<unsigned> NumOpcodes = None);
class ClusterId {
public:
static ClusterId noise() { return ClusterId(kNoise); }
static ClusterId error() { return ClusterId(kError); }
static ClusterId makeValid(size_t Id, bool IsUnstable = false) {
return ClusterId(Id, IsUnstable);
}
static ClusterId makeValidUnstable(size_t Id) {
return makeValid(Id, /*IsUnstable=*/true);
}
ClusterId() : Id_(kUndef), IsUnstable_(false) {}
// Compare id's, ignoring the 'unstability' bit.
bool operator==(const ClusterId &O) const { return Id_ == O.Id_; }
bool operator<(const ClusterId &O) const { return Id_ < O.Id_; }
bool isValid() const { return Id_ <= kMaxValid; }
bool isUnstable() const { return IsUnstable_; }
bool isNoise() const { return Id_ == kNoise; }
bool isError() const { return Id_ == kError; }
bool isUndef() const { return Id_ == kUndef; }
// Precondition: isValid().
size_t getId() const {
assert(isValid());
return Id_;
}
private:
ClusterId(size_t Id, bool IsUnstable = false)
: Id_(Id), IsUnstable_(IsUnstable) {}
static constexpr const size_t kMaxValid =
(std::numeric_limits<size_t>::max() >> 1) - 4;
static constexpr const size_t kNoise = kMaxValid + 1;
static constexpr const size_t kError = kMaxValid + 2;
static constexpr const size_t kUndef = kMaxValid + 3;
size_t Id_ : (std::numeric_limits<size_t>::digits - 1);
size_t IsUnstable_ : 1;
};
static_assert(sizeof(ClusterId) == sizeof(size_t), "should be a bit field.");
struct Cluster {
Cluster() = delete;
explicit Cluster(const ClusterId &Id) : Id(Id) {}
const ClusterId Id;
// Indices of benchmarks within the cluster.
std::vector<int> PointIndices;
};
ClusterId getClusterIdForPoint(size_t P) const {
return ClusterIdForPoint_[P];
}
const std::vector<InstructionBenchmark> &getPoints() const { return Points_; }
const Cluster &getCluster(ClusterId Id) const {
assert(!Id.isUndef() && "unlabeled cluster");
if (Id.isNoise()) {
return NoiseCluster_;
}
if (Id.isError()) {
return ErrorCluster_;
}
return Clusters_[Id.getId()];
}
const std::vector<Cluster> &getValidClusters() const { return Clusters_; }
// Returns true if the given point is within a distance Epsilon of each other.
bool isNeighbour(const std::vector<BenchmarkMeasure> &P,
const std::vector<BenchmarkMeasure> &Q,
const double EpsilonSquared_) const {
double DistanceSquared = 0.0;
for (size_t I = 0, E = P.size(); I < E; ++I) {
const auto Diff = P[I].PerInstructionValue - Q[I].PerInstructionValue;
DistanceSquared += Diff * Diff;
}
return DistanceSquared <= EpsilonSquared_;
}
private:
InstructionBenchmarkClustering(
const std::vector<InstructionBenchmark> &Points,
double AnalysisClusteringEpsilonSquared);
Error validateAndSetup();
void clusterizeDbScan(size_t MinPts);
void clusterizeNaive(unsigned NumOpcodes);
// Stabilization is only needed if dbscan was used to clusterize.
void stabilize(unsigned NumOpcodes);
void rangeQuery(size_t Q, std::vector<size_t> &Scratchpad) const;
bool areAllNeighbours(ArrayRef<size_t> Pts) const;
const std::vector<InstructionBenchmark> &Points_;
const double AnalysisClusteringEpsilonSquared_;
int NumDimensions_ = 0;
// ClusterForPoint_[P] is the cluster id for Points[P].
std::vector<ClusterId> ClusterIdForPoint_;
std::vector<Cluster> Clusters_;
Cluster NoiseCluster_;
Cluster ErrorCluster_;
};
class SchedClassClusterCentroid {
public:
const std::vector<PerInstructionStats> &getStats() const {
return Representative;
}
std::vector<BenchmarkMeasure> getAsPoint() const;
void addPoint(ArrayRef<BenchmarkMeasure> Point);
bool validate(InstructionBenchmark::ModeE Mode) const;
private:
// Measurement stats for the points in the SchedClassCluster.
std::vector<PerInstructionStats> Representative;
};
} // namespace exegesis
} // namespace llvm
#endif // LLVM_TOOLS_LLVM_EXEGESIS_CLUSTERING_H
|