You can not select more than 25 topics
			Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
		
		
		
		
		
			
		
			
				
					
					
						
							220 lines
						
					
					
						
							6.0 KiB
						
					
					
				
			
		
		
		
			
			
			
				
					
				
				
					
				
			
		
		
	
	
							220 lines
						
					
					
						
							6.0 KiB
						
					
					
				| 
 | |
| //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 | |
| // -I /home/gael/Coding/LinearAlgebra/CSparse/Include/ /home/gael/Coding/LinearAlgebra/CSparse/Lib/libcsparse.a | |
|  | |
| #ifndef SIZE | |
| #define SIZE 10000 | |
| #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(); } | |
|  | |
| typedef SparseMatrix<Scalar,UpperTriangular> EigenSparseTriMatrix; | |
| typedef SparseMatrix<Scalar,RowMajorBit|UpperTriangular> EigenSparseTriMatrixRow; | |
| 
 | |
| void fillMatrix(float density, int rows, int cols,  EigenSparseTriMatrix& dst) | |
| { | |
|   dst.startFill(rows*cols*density); | |
|   for(int j = 0; j < cols; j++) | |
|   { | |
|     for(int i = 0; i < j; i++) | |
|     { | |
|       Scalar v = (internal::random<float>(0,1) < density) ? internal::random<Scalar>() : 0; | |
|       if (v!=0) | |
|         dst.fill(i,j) = v; | |
|     } | |
|     dst.fill(j,j) = internal::random<Scalar>(); | |
|   } | |
|   dst.endFill(); | |
| } | |
| 
 | |
| int main(int argc, char *argv[]) | |
| { | |
|   int rows = SIZE; | |
|   int cols = SIZE; | |
|   float density = DENSITY; | |
|   BenchTimer timer; | |
|   #if 1 | |
|   EigenSparseTriMatrix sm1(rows,cols); | |
|   typedef Matrix<Scalar,Dynamic,1> DenseVector; | |
|   DenseVector b = DenseVector::Random(cols); | |
|   DenseVector x = DenseVector::Random(cols); | |
| 
 | |
|   bool densedone = false; | |
| 
 | |
|   for (float density = DENSITY; density>=MINDENSITY; density*=0.5) | |
|   { | |
|     EigenSparseTriMatrix sm1(rows, cols); | |
|     fillMatrix(density, rows, cols, sm1); | |
| 
 | |
|     // dense matrices | |
|     #ifdef DENSEMATRIX | |
|     if (!densedone) | |
|     { | |
|       densedone = true; | |
|       std::cout << "Eigen Dense\t" << density*100 << "%\n"; | |
|       DenseMatrix m1(rows,cols); | |
|       Matrix<Scalar,Dynamic,Dynamic,Dynamic,Dynamic,RowMajorBit> m2(rows,cols); | |
|       eiToDense(sm1, m1); | |
|       m2 = m1; | |
| 
 | |
|       BENCH(x = m1.marked<UpperTriangular>().solveTriangular(b);) | |
|       std::cout << "   colmajor^-1 * b:\t" << timer.value() << endl; | |
| //       std::cerr << x.transpose() << "\n"; | |
|  | |
|       BENCH(x = m2.marked<UpperTriangular>().solveTriangular(b);) | |
|       std::cout << "   rowmajor^-1 * b:\t" << timer.value() << endl; | |
| //       std::cerr << x.transpose() << "\n"; | |
|     } | |
|     #endif | |
|  | |
|     // eigen sparse matrices | |
|     { | |
|       std::cout << "Eigen sparse\t" << density*100 << "%\n"; | |
|       EigenSparseTriMatrixRow sm2 = sm1; | |
| 
 | |
|       BENCH(x = sm1.solveTriangular(b);) | |
|       std::cout << "   colmajor^-1 * b:\t" << timer.value() << endl; | |
| //       std::cerr << x.transpose() << "\n"; | |
|  | |
|       BENCH(x = sm2.solveTriangular(b);) | |
|       std::cout << "   rowmajor^-1 * b:\t" << timer.value() << endl; | |
| //       std::cerr << x.transpose() << "\n"; | |
|  | |
| //       x = b; | |
| //       BENCH(sm1.inverseProductInPlace(x);) | |
| //       std::cout << "   colmajor^-1 * b:\t" << timer.value() << " (inplace)" << endl; | |
| //       std::cerr << x.transpose() << "\n"; | |
| // | |
| //       x = b; | |
| //       BENCH(sm2.inverseProductInPlace(x);) | |
| //       std::cout << "   rowmajor^-1 * b:\t" << timer.value() << " (inplace)" << endl; | |
| //       std::cerr << x.transpose() << "\n"; | |
|     } | |
| 
 | |
| 
 | |
| 
 | |
|     // CSparse | |
|     #ifdef CSPARSE | |
|     { | |
|       std::cout << "CSparse \t" << density*100 << "%\n"; | |
|       cs *m1; | |
|       eiToCSparse(sm1, m1); | |
| 
 | |
|       BENCH(x = b; if (!cs_lsolve (m1, x.data())){std::cerr << "cs_lsolve failed\n"; break;}; ) | |
|       std::cout << "   colmajor^-1 * b:\t" << timer.value() << endl; | |
|     } | |
|     #endif | |
|  | |
|     // GMM++ | |
|     #ifndef NOGMM | |
|     { | |
|       std::cout << "GMM++ sparse\t" << density*100 << "%\n"; | |
|       GmmSparse m1(rows,cols); | |
|       gmm::csr_matrix<Scalar> m2; | |
|       eiToGmm(sm1, m1); | |
|       gmm::copy(m1,m2); | |
|       std::vector<Scalar> gmmX(cols), gmmB(cols); | |
|       Map<Matrix<Scalar,Dynamic,1> >(&gmmX[0], cols) = x; | |
|       Map<Matrix<Scalar,Dynamic,1> >(&gmmB[0], cols) = b; | |
| 
 | |
|       gmmX = gmmB; | |
|       BENCH(gmm::upper_tri_solve(m1, gmmX, false);) | |
|       std::cout << "   colmajor^-1 * b:\t" << timer.value() << endl; | |
| //       std::cerr << Map<Matrix<Scalar,Dynamic,1> >(&gmmX[0], cols).transpose() << "\n"; | |
|  | |
|       gmmX = gmmB; | |
|       BENCH(gmm::upper_tri_solve(m2, gmmX, false);) | |
|       timer.stop(); | |
|       std::cout << "   rowmajor^-1 * b:\t" << timer.value() << endl; | |
| //       std::cerr << Map<Matrix<Scalar,Dynamic,1> >(&gmmX[0], cols).transpose() << "\n"; | |
|     } | |
|     #endif | |
|  | |
|     // MTL4 | |
|     #ifndef NOMTL | |
|     { | |
|       std::cout << "MTL4\t" << density*100 << "%\n"; | |
|       MtlSparse m1(rows,cols); | |
|       MtlSparseRowMajor m2(rows,cols); | |
|       eiToMtl(sm1, m1); | |
|       m2 = m1; | |
|       mtl::dense_vector<Scalar> x(rows, 1.0); | |
|       mtl::dense_vector<Scalar> b(rows, 1.0); | |
| 
 | |
|       BENCH(x = mtl::upper_trisolve(m1,b);) | |
|       std::cout << "   colmajor^-1 * b:\t" << timer.value() << endl; | |
| //       std::cerr << x << "\n"; | |
|  | |
|       BENCH(x = mtl::upper_trisolve(m2,b);) | |
|       std::cout << "   rowmajor^-1 * b:\t" << timer.value() << endl; | |
| //       std::cerr << x << "\n"; | |
|     } | |
|     #endif | |
|  | |
| 
 | |
|     std::cout << "\n\n"; | |
|   } | |
|   #endif | |
|  | |
|   #if 0 | |
|     // bench small matrices (in-place versus return bye value) | |
|     { | |
|       timer.reset(); | |
|       for (int _j=0; _j<10; ++_j) { | |
|         Matrix4f m = Matrix4f::Random(); | |
|         Vector4f b = Vector4f::Random(); | |
|         Vector4f x = Vector4f::Random(); | |
|         timer.start(); | |
|         for (int _k=0; _k<1000000; ++_k) { | |
|           b = m.inverseProduct(b); | |
|         } | |
|         timer.stop(); | |
|       } | |
|       std::cout << "4x4 :\t" << timer.value() << endl; | |
|     } | |
|  | |
|     { | |
|       timer.reset(); | |
|       for (int _j=0; _j<10; ++_j) { | |
|         Matrix4f m = Matrix4f::Random(); | |
|         Vector4f b = Vector4f::Random(); | |
|         Vector4f x = Vector4f::Random(); | |
|         timer.start(); | |
|         for (int _k=0; _k<1000000; ++_k) { | |
|           m.inverseProductInPlace(x); | |
|         } | |
|         timer.stop(); | |
|       } | |
|       std::cout << "4x4 IP :\t" << timer.value() << endl; | |
|     } | |
|   #endif | |
|  | |
|   return 0; | |
| } | |
| 
 |