diff options
Diffstat (limited to 'bench/sparse_dense_product.cpp')
| -rw-r--r-- | bench/sparse_dense_product.cpp | 187 |
1 files changed, 187 insertions, 0 deletions
diff --git a/bench/sparse_dense_product.cpp b/bench/sparse_dense_product.cpp new file mode 100644 index 000000000..f3f519406 --- /dev/null +++ b/bench/sparse_dense_product.cpp @@ -0,0 +1,187 @@ + +//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; +} + |