sparse_trisolver.cpp
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1 
2 //g++ -O3 -g0 -DNDEBUG sparse_product.cpp -I.. -I/home/gael/Coding/LinearAlgebra/mtl4/ -DDENSITY=0.005 -DSIZE=10000 && ./a.out
3 //g++ -O3 -g0 -DNDEBUG sparse_product.cpp -I.. -I/home/gael/Coding/LinearAlgebra/mtl4/ -DDENSITY=0.05 -DSIZE=2000 && ./a.out
4 // -DNOGMM -DNOMTL
5 // -I /home/gael/Coding/LinearAlgebra/CSparse/Include/ /home/gael/Coding/LinearAlgebra/CSparse/Lib/libcsparse.a
6 
7 #ifndef SIZE
8 #define SIZE 10000
9 #endif
10 
11 #ifndef DENSITY
12 #define DENSITY 0.01
13 #endif
14 
15 #ifndef REPEAT
16 #define REPEAT 1
17 #endif
18 
19 #include "BenchSparseUtil.h"
20 
21 #ifndef MINDENSITY
22 #define MINDENSITY 0.0004
23 #endif
24 
25 #ifndef NBTRIES
26 #define NBTRIES 10
27 #endif
28 
29 #define BENCH(X) \
30  timer.reset(); \
31  for (int _j=0; _j<NBTRIES; ++_j) { \
32  timer.start(); \
33  for (int _k=0; _k<REPEAT; ++_k) { \
34  X \
35  } timer.stop(); }
36 
39 
40 void fillMatrix(float density, int rows, int cols, EigenSparseTriMatrix& dst)
41 {
42  dst.startFill(rows*cols*density);
43  for(int j = 0; j < cols; j++)
44  {
45  for(int i = 0; i < j; i++)
46  {
47  Scalar v = (internal::random<float>(0,1) < density) ? internal::random<Scalar>() : 0;
48  if (v!=0)
49  dst.fill(i,j) = v;
50  }
51  dst.fill(j,j) = internal::random<Scalar>();
52  }
53  dst.endFill();
54 }
55 
56 int main(int argc, char *argv[])
57 {
58  int rows = SIZE;
59  int cols = SIZE;
60  float density = DENSITY;
62  #if 1
65  DenseVector b = DenseVector::Random(cols);
66  DenseVector x = DenseVector::Random(cols);
67 
68  bool densedone = false;
69 
70  for (float density = DENSITY; density>=MINDENSITY; density*=0.5)
71  {
73  fillMatrix(density, rows, cols, sm1);
74 
75  // dense matrices
76  #ifdef DENSEMATRIX
77  if (!densedone)
78  {
79  densedone = true;
80  std::cout << "Eigen Dense\t" << density*100 << "%\n";
83  eiToDense(sm1, m1);
84  m2 = m1;
85 
86  BENCH(x = m1.marked<UpperTriangular>().solveTriangular(b);)
87  std::cout << " colmajor^-1 * b:\t" << timer.value() << endl;
88 // std::cerr << x.transpose() << "\n";
89 
90  BENCH(x = m2.marked<UpperTriangular>().solveTriangular(b);)
91  std::cout << " rowmajor^-1 * b:\t" << timer.value() << endl;
92 // std::cerr << x.transpose() << "\n";
93  }
94  #endif
95 
96  // eigen sparse matrices
97  {
98  std::cout << "Eigen sparse\t" << density*100 << "%\n";
99  EigenSparseTriMatrixRow sm2 = sm1;
100 
101  BENCH(x = sm1.solveTriangular(b);)
102  std::cout << " colmajor^-1 * b:\t" << timer.value() << endl;
103 // std::cerr << x.transpose() << "\n";
104 
105  BENCH(x = sm2.solveTriangular(b);)
106  std::cout << " rowmajor^-1 * b:\t" << timer.value() << endl;
107 // std::cerr << x.transpose() << "\n";
108 
109 // x = b;
110 // BENCH(sm1.inverseProductInPlace(x);)
111 // std::cout << " colmajor^-1 * b:\t" << timer.value() << " (inplace)" << endl;
112 // std::cerr << x.transpose() << "\n";
113 //
114 // x = b;
115 // BENCH(sm2.inverseProductInPlace(x);)
116 // std::cout << " rowmajor^-1 * b:\t" << timer.value() << " (inplace)" << endl;
117 // std::cerr << x.transpose() << "\n";
118  }
119 
120 
121 
122  // CSparse
123  #ifdef CSPARSE
124  {
125  std::cout << "CSparse \t" << density*100 << "%\n";
126  cs *m1;
127  eiToCSparse(sm1, m1);
128 
129  BENCH(x = b; if (!cs_lsolve (m1, x.data())){std::cerr << "cs_lsolve failed\n"; break;}; )
130  std::cout << " colmajor^-1 * b:\t" << timer.value() << endl;
131  }
132  #endif
133 
134  // GMM++
135  #ifndef NOGMM
136  {
137  std::cout << "GMM++ sparse\t" << density*100 << "%\n";
139  gmm::csr_matrix<Scalar> m2;
140  eiToGmm(sm1, m1);
141  gmm::copy(m1,m2);
142  std::vector<Scalar> gmmX(cols), gmmB(cols);
143  Map<Matrix<Scalar,Dynamic,1> >(&gmmX[0], cols) = x;
144  Map<Matrix<Scalar,Dynamic,1> >(&gmmB[0], cols) = b;
145 
146  gmmX = gmmB;
147  BENCH(gmm::upper_tri_solve(m1, gmmX, false);)
148  std::cout << " colmajor^-1 * b:\t" << timer.value() << endl;
149 // std::cerr << Map<Matrix<Scalar,Dynamic,1> >(&gmmX[0], cols).transpose() << "\n";
150 
151  gmmX = gmmB;
152  BENCH(gmm::upper_tri_solve(m2, gmmX, false);)
153  timer.stop();
154  std::cout << " rowmajor^-1 * b:\t" << timer.value() << endl;
155 // std::cerr << Map<Matrix<Scalar,Dynamic,1> >(&gmmX[0], cols).transpose() << "\n";
156  }
157  #endif
158 
159  // MTL4
160  #ifndef NOMTL
161  {
162  std::cout << "MTL4\t" << density*100 << "%\n";
165  eiToMtl(sm1, m1);
166  m2 = m1;
167  mtl::dense_vector<Scalar> x(rows, 1.0);
168  mtl::dense_vector<Scalar> b(rows, 1.0);
169 
170  BENCH(x = mtl::upper_trisolve(m1,b);)
171  std::cout << " colmajor^-1 * b:\t" << timer.value() << endl;
172 // std::cerr << x << "\n";
173 
174  BENCH(x = mtl::upper_trisolve(m2,b);)
175  std::cout << " rowmajor^-1 * b:\t" << timer.value() << endl;
176 // std::cerr << x << "\n";
177  }
178  #endif
179 
180 
181  std::cout << "\n\n";
182  }
183  #endif
184 
185  #if 0
186  // bench small matrices (in-place versus return bye value)
187  {
188  timer.reset();
189  for (int _j=0; _j<10; ++_j) {
190  Matrix4f m = Matrix4f::Random();
191  Vector4f b = Vector4f::Random();
192  Vector4f x = Vector4f::Random();
193  timer.start();
194  for (int _k=0; _k<1000000; ++_k) {
195  b = m.inverseProduct(b);
196  }
197  timer.stop();
198  }
199  std::cout << "4x4 :\t" << timer.value() << endl;
200  }
201 
202  {
203  timer.reset();
204  for (int _j=0; _j<10; ++_j) {
205  Matrix4f m = Matrix4f::Random();
206  Vector4f b = Vector4f::Random();
207  Vector4f x = Vector4f::Random();
208  timer.start();
209  for (int _k=0; _k<1000000; ++_k) {
210  m.inverseProductInPlace(x);
211  }
212  timer.stop();
213  }
214  std::cout << "4x4 IP :\t" << timer.value() << endl;
215  }
216  #endif
217 
218  return 0;
219 }
220 
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autogenerated on Sat Nov 16 2024 04:04:24