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Diffstat (limited to 'eigen/bench/sparse_dense_product.cpp')
| -rw-r--r-- | eigen/bench/sparse_dense_product.cpp | 187 |
1 files changed, 0 insertions, 187 deletions
diff --git a/eigen/bench/sparse_dense_product.cpp b/eigen/bench/sparse_dense_product.cpp deleted file mode 100644 index f3f5194..0000000 --- a/eigen/bench/sparse_dense_product.cpp +++ /dev/null @@ -1,187 +0,0 @@ - -//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; -} - |