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
|
//g++ -O3 -g0 -DNDEBUG sparse_product.cpp -I.. -I/home/gael/Coding/LinearAlgebra/mtl4/ -DDENSITY=0.005 -DSIZE=10000 && ./a.out
//g++ -O3 -g0 -DNDEBUG sparse_product.cpp -I.. -I/home/gael/Coding/LinearAlgebra/mtl4/ -DDENSITY=0.05 -DSIZE=2000 && ./a.out
// -DNOGMM -DNOMTL -DCSPARSE
// -I /home/gael/Coding/LinearAlgebra/CSparse/Include/ /home/gael/Coding/LinearAlgebra/CSparse/Lib/libcsparse.a
#ifndef SIZE
#define SIZE 650000
#endif
#ifndef DENSITY
#define DENSITY 0.01
#endif
#ifndef REPEAT
#define REPEAT 1
#endif
#include "BenchSparseUtil.h"
#ifndef MINDENSITY
#define MINDENSITY 0.0004
#endif
#ifndef NBTRIES
#define NBTRIES 10
#endif
#define BENCH(X) \
timer.reset(); \
for (int _j=0; _j<NBTRIES; ++_j) { \
timer.start(); \
for (int _k=0; _k<REPEAT; ++_k) { \
X \
} timer.stop(); }
#ifdef CSPARSE
cs* cs_sorted_multiply(const cs* a, const cs* b)
{
cs* A = cs_transpose (a, 1) ;
cs* B = cs_transpose (b, 1) ;
cs* D = cs_multiply (B,A) ; /* D = B'*A' */
cs_spfree (A) ;
cs_spfree (B) ;
cs_dropzeros (D) ; /* drop zeros from D */
cs* C = cs_transpose (D, 1) ; /* C = D', so that C is sorted */
cs_spfree (D) ;
return C;
}
#endif
int main(int argc, char *argv[])
{
int rows = SIZE;
int cols = SIZE;
float density = DENSITY;
EigenSparseMatrix sm1(rows,cols);
DenseVector v1(cols), v2(cols);
v1.setRandom();
BenchTimer timer;
for (float density = DENSITY; density>=MINDENSITY; density*=0.5)
{
//fillMatrix(density, rows, cols, sm1);
fillMatrix2(7, rows, cols, sm1);
// dense matrices
#ifdef DENSEMATRIX
{
std::cout << "Eigen Dense\t" << density*100 << "%\n";
DenseMatrix m1(rows,cols);
eiToDense(sm1, m1);
timer.reset();
timer.start();
for (int k=0; k<REPEAT; ++k)
v2 = m1 * v1;
timer.stop();
std::cout << " a * v:\t" << timer.best() << " " << double(REPEAT)/timer.best() << " * / sec " << endl;
timer.reset();
timer.start();
for (int k=0; k<REPEAT; ++k)
v2 = m1.transpose() * v1;
timer.stop();
std::cout << " a' * v:\t" << timer.best() << endl;
}
#endif
// eigen sparse matrices
{
std::cout << "Eigen sparse\t" << sm1.nonZeros()/float(sm1.rows()*sm1.cols())*100 << "%\n";
BENCH(asm("#myc"); v2 = sm1 * v1; asm("#myd");)
std::cout << " a * v:\t" << timer.best()/REPEAT << " " << double(REPEAT)/timer.best(REAL_TIMER) << " * / sec " << endl;
BENCH( { asm("#mya"); v2 = sm1.transpose() * v1; asm("#myb"); })
std::cout << " a' * v:\t" << timer.best()/REPEAT << endl;
}
// {
// DynamicSparseMatrix<Scalar> m1(sm1);
// std::cout << "Eigen dyn-sparse\t" << m1.nonZeros()/float(m1.rows()*m1.cols())*100 << "%\n";
//
// BENCH(for (int k=0; k<REPEAT; ++k) v2 = m1 * v1;)
// std::cout << " a * v:\t" << timer.value() << endl;
//
// BENCH(for (int k=0; k<REPEAT; ++k) v2 = m1.transpose() * v1;)
// std::cout << " a' * v:\t" << timer.value() << endl;
// }
// GMM++
#ifndef NOGMM
{
std::cout << "GMM++ sparse\t" << density*100 << "%\n";
//GmmDynSparse gmmT3(rows,cols);
GmmSparse m1(rows,cols);
eiToGmm(sm1, m1);
std::vector<Scalar> gmmV1(cols), gmmV2(cols);
Map<Matrix<Scalar,Dynamic,1> >(&gmmV1[0], cols) = v1;
Map<Matrix<Scalar,Dynamic,1> >(&gmmV2[0], cols) = v2;
BENCH( asm("#myx"); gmm::mult(m1, gmmV1, gmmV2); asm("#myy"); )
std::cout << " a * v:\t" << timer.value() << endl;
BENCH( gmm::mult(gmm::transposed(m1), gmmV1, gmmV2); )
std::cout << " a' * v:\t" << timer.value() << endl;
}
#endif
#ifndef NOUBLAS
{
std::cout << "ublas sparse\t" << density*100 << "%\n";
UBlasSparse m1(rows,cols);
eiToUblas(sm1, m1);
boost::numeric::ublas::vector<Scalar> uv1, uv2;
eiToUblasVec(v1,uv1);
eiToUblasVec(v2,uv2);
// std::vector<Scalar> gmmV1(cols), gmmV2(cols);
// Map<Matrix<Scalar,Dynamic,1> >(&gmmV1[0], cols) = v1;
// Map<Matrix<Scalar,Dynamic,1> >(&gmmV2[0], cols) = v2;
BENCH( uv2 = boost::numeric::ublas::prod(m1, uv1); )
std::cout << " a * v:\t" << timer.value() << endl;
// BENCH( boost::ublas::prod(gmm::transposed(m1), gmmV1, gmmV2); )
// std::cout << " a' * v:\t" << timer.value() << endl;
}
#endif
// MTL4
#ifndef NOMTL
{
std::cout << "MTL4\t" << density*100 << "%\n";
MtlSparse m1(rows,cols);
eiToMtl(sm1, m1);
mtl::dense_vector<Scalar> mtlV1(cols, 1.0);
mtl::dense_vector<Scalar> mtlV2(cols, 1.0);
timer.reset();
timer.start();
for (int k=0; k<REPEAT; ++k)
mtlV2 = m1 * mtlV1;
timer.stop();
std::cout << " a * v:\t" << timer.value() << endl;
timer.reset();
timer.start();
for (int k=0; k<REPEAT; ++k)
mtlV2 = trans(m1) * mtlV1;
timer.stop();
std::cout << " a' * v:\t" << timer.value() << endl;
}
#endif
std::cout << "\n\n";
}
return 0;
}
|
