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Diffstat (limited to 'bench/sparse_setter.cpp')
| -rw-r--r-- | bench/sparse_setter.cpp | 485 |
1 files changed, 485 insertions, 0 deletions
diff --git a/bench/sparse_setter.cpp b/bench/sparse_setter.cpp new file mode 100644 index 000000000..a9f0b11cc --- /dev/null +++ b/bench/sparse_setter.cpp @@ -0,0 +1,485 @@ + +//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 100000 +#endif + +#ifndef NBPERROW +#define NBPERROW 24 +#endif + +#ifndef REPEAT +#define REPEAT 2 +#endif + +#ifndef NBTRIES +#define NBTRIES 2 +#endif + +#ifndef KK +#define KK 10 +#endif + +#ifndef NOGOOGLE +#define EIGEN_GOOGLEHASH_SUPPORT +#include <google/sparse_hash_map> +#endif + +#include "BenchSparseUtil.h" + +#define CHECK_MEM +// #define CHECK_MEM std/**/::cout << "check mem\n"; getchar(); + +#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 std::vector<Vector2i> Coordinates; +typedef std::vector<float> Values; + +EIGEN_DONT_INLINE Scalar* setinnerrand_eigen(const Coordinates& coords, const Values& vals); +EIGEN_DONT_INLINE Scalar* setrand_eigen_dynamic(const Coordinates& coords, const Values& vals); +EIGEN_DONT_INLINE Scalar* setrand_eigen_compact(const Coordinates& coords, const Values& vals); +EIGEN_DONT_INLINE Scalar* setrand_eigen_sumeq(const Coordinates& coords, const Values& vals); +EIGEN_DONT_INLINE Scalar* setrand_eigen_gnu_hash(const Coordinates& coords, const Values& vals); +EIGEN_DONT_INLINE Scalar* setrand_eigen_google_dense(const Coordinates& coords, const Values& vals); +EIGEN_DONT_INLINE Scalar* setrand_eigen_google_sparse(const Coordinates& coords, const Values& vals); +EIGEN_DONT_INLINE Scalar* setrand_scipy(const Coordinates& coords, const Values& vals); +EIGEN_DONT_INLINE Scalar* setrand_ublas_mapped(const Coordinates& coords, const Values& vals); +EIGEN_DONT_INLINE Scalar* setrand_ublas_coord(const Coordinates& coords, const Values& vals); +EIGEN_DONT_INLINE Scalar* setrand_ublas_compressed(const Coordinates& coords, const Values& vals); +EIGEN_DONT_INLINE Scalar* setrand_ublas_genvec(const Coordinates& coords, const Values& vals); +EIGEN_DONT_INLINE Scalar* setrand_mtl(const Coordinates& coords, const Values& vals); + +int main(int argc, char *argv[]) +{ + int rows = SIZE; + int cols = SIZE; + bool fullyrand = true; + + BenchTimer timer; + Coordinates coords; + Values values; + if(fullyrand) + { + Coordinates pool; + pool.reserve(cols*NBPERROW); + std::cerr << "fill pool" << "\n"; + for (int i=0; i<cols*NBPERROW; ) + { +// DynamicSparseMatrix<int> stencil(SIZE,SIZE); + Vector2i ij(internal::random<int>(0,rows-1),internal::random<int>(0,cols-1)); +// if(stencil.coeffRef(ij.x(), ij.y())==0) + { +// stencil.coeffRef(ij.x(), ij.y()) = 1; + pool.push_back(ij); + + } + ++i; + } + std::cerr << "pool ok" << "\n"; + int n = cols*NBPERROW*KK; + coords.reserve(n); + values.reserve(n); + for (int i=0; i<n; ++i) + { + int i = internal::random<int>(0,pool.size()); + coords.push_back(pool[i]); + values.push_back(internal::random<Scalar>()); + } + } + else + { + for (int j=0; j<cols; ++j) + for (int i=0; i<NBPERROW; ++i) + { + coords.push_back(Vector2i(internal::random<int>(0,rows-1),j)); + values.push_back(internal::random<Scalar>()); + } + } + std::cout << "nnz = " << coords.size() << "\n"; + CHECK_MEM + + // dense matrices + #ifdef DENSEMATRIX + { + BENCH(setrand_eigen_dense(coords,values);) + std::cout << "Eigen Dense\t" << timer.value() << "\n"; + } + #endif + + // eigen sparse matrices +// if (!fullyrand) +// { +// BENCH(setinnerrand_eigen(coords,values);) +// std::cout << "Eigen fillrand\t" << timer.value() << "\n"; +// } + { + BENCH(setrand_eigen_dynamic(coords,values);) + std::cout << "Eigen dynamic\t" << timer.value() << "\n"; + } +// { +// BENCH(setrand_eigen_compact(coords,values);) +// std::cout << "Eigen compact\t" << timer.value() << "\n"; +// } + { + BENCH(setrand_eigen_sumeq(coords,values);) + std::cout << "Eigen sumeq\t" << timer.value() << "\n"; + } + { +// BENCH(setrand_eigen_gnu_hash(coords,values);) +// std::cout << "Eigen std::map\t" << timer.value() << "\n"; + } + { + BENCH(setrand_scipy(coords,values);) + std::cout << "scipy\t" << timer.value() << "\n"; + } + #ifndef NOGOOGLE + { + BENCH(setrand_eigen_google_dense(coords,values);) + std::cout << "Eigen google dense\t" << timer.value() << "\n"; + } + { + BENCH(setrand_eigen_google_sparse(coords,values);) + std::cout << "Eigen google sparse\t" << timer.value() << "\n"; + } + #endif + + #ifndef NOUBLAS + { +// BENCH(setrand_ublas_mapped(coords,values);) +// std::cout << "ublas mapped\t" << timer.value() << "\n"; + } + { + BENCH(setrand_ublas_genvec(coords,values);) + std::cout << "ublas vecofvec\t" << timer.value() << "\n"; + } + /*{ + timer.reset(); + timer.start(); + for (int k=0; k<REPEAT; ++k) + setrand_ublas_compressed(coords,values); + timer.stop(); + std::cout << "ublas comp\t" << timer.value() << "\n"; + } + { + timer.reset(); + timer.start(); + for (int k=0; k<REPEAT; ++k) + setrand_ublas_coord(coords,values); + timer.stop(); + std::cout << "ublas coord\t" << timer.value() << "\n"; + }*/ + #endif + + + // MTL4 + #ifndef NOMTL + { + BENCH(setrand_mtl(coords,values)); + std::cout << "MTL\t" << timer.value() << "\n"; + } + #endif + + return 0; +} + +EIGEN_DONT_INLINE Scalar* setinnerrand_eigen(const Coordinates& coords, const Values& vals) +{ + using namespace Eigen; + SparseMatrix<Scalar> mat(SIZE,SIZE); + //mat.startFill(2000000/*coords.size()*/); + for (int i=0; i<coords.size(); ++i) + { + mat.insert(coords[i].x(), coords[i].y()) = vals[i]; + } + mat.finalize(); + CHECK_MEM; + return 0; +} + +EIGEN_DONT_INLINE Scalar* setrand_eigen_dynamic(const Coordinates& coords, const Values& vals) +{ + using namespace Eigen; + DynamicSparseMatrix<Scalar> mat(SIZE,SIZE); + mat.reserve(coords.size()/10); + for (int i=0; i<coords.size(); ++i) + { + mat.coeffRef(coords[i].x(), coords[i].y()) += vals[i]; + } + mat.finalize(); + CHECK_MEM; + return &mat.coeffRef(coords[0].x(), coords[0].y()); +} + +EIGEN_DONT_INLINE Scalar* setrand_eigen_sumeq(const Coordinates& coords, const Values& vals) +{ + using namespace Eigen; + int n = coords.size()/KK; + DynamicSparseMatrix<Scalar> mat(SIZE,SIZE); + for (int j=0; j<KK; ++j) + { + DynamicSparseMatrix<Scalar> aux(SIZE,SIZE); + mat.reserve(n); + for (int i=j*n; i<(j+1)*n; ++i) + { + aux.insert(coords[i].x(), coords[i].y()) += vals[i]; + } + aux.finalize(); + mat += aux; + } + return &mat.coeffRef(coords[0].x(), coords[0].y()); +} + +EIGEN_DONT_INLINE Scalar* setrand_eigen_compact(const Coordinates& coords, const Values& vals) +{ + using namespace Eigen; + DynamicSparseMatrix<Scalar> setter(SIZE,SIZE); + setter.reserve(coords.size()/10); + for (int i=0; i<coords.size(); ++i) + { + setter.coeffRef(coords[i].x(), coords[i].y()) += vals[i]; + } + SparseMatrix<Scalar> mat = setter; + CHECK_MEM; + return &mat.coeffRef(coords[0].x(), coords[0].y()); +} + +EIGEN_DONT_INLINE Scalar* setrand_eigen_gnu_hash(const Coordinates& coords, const Values& vals) +{ + using namespace Eigen; + SparseMatrix<Scalar> mat(SIZE,SIZE); + { + RandomSetter<SparseMatrix<Scalar>, StdMapTraits > setter(mat); + for (int i=0; i<coords.size(); ++i) + { + setter(coords[i].x(), coords[i].y()) += vals[i]; + } + CHECK_MEM; + } + return &mat.coeffRef(coords[0].x(), coords[0].y()); +} + +#ifndef NOGOOGLE +EIGEN_DONT_INLINE Scalar* setrand_eigen_google_dense(const Coordinates& coords, const Values& vals) +{ + using namespace Eigen; + SparseMatrix<Scalar> mat(SIZE,SIZE); + { + RandomSetter<SparseMatrix<Scalar>, GoogleDenseHashMapTraits> setter(mat); + for (int i=0; i<coords.size(); ++i) + setter(coords[i].x(), coords[i].y()) += vals[i]; + CHECK_MEM; + } + return &mat.coeffRef(coords[0].x(), coords[0].y()); +} + +EIGEN_DONT_INLINE Scalar* setrand_eigen_google_sparse(const Coordinates& coords, const Values& vals) +{ + using namespace Eigen; + SparseMatrix<Scalar> mat(SIZE,SIZE); + { + RandomSetter<SparseMatrix<Scalar>, GoogleSparseHashMapTraits> setter(mat); + for (int i=0; i<coords.size(); ++i) + setter(coords[i].x(), coords[i].y()) += vals[i]; + CHECK_MEM; + } + return &mat.coeffRef(coords[0].x(), coords[0].y()); +} +#endif + + +template <class T> +void coo_tocsr(const int n_row, + const int n_col, + const int nnz, + const Coordinates Aij, + const Values Ax, + int Bp[], + int Bj[], + T Bx[]) +{ + //compute number of non-zero entries per row of A coo_tocsr + std::fill(Bp, Bp + n_row, 0); + + for (int n = 0; n < nnz; n++){ + Bp[Aij[n].x()]++; + } + + //cumsum the nnz per row to get Bp[] + for(int i = 0, cumsum = 0; i < n_row; i++){ + int temp = Bp[i]; + Bp[i] = cumsum; + cumsum += temp; + } + Bp[n_row] = nnz; + + //write Aj,Ax into Bj,Bx + for(int n = 0; n < nnz; n++){ + int row = Aij[n].x(); + int dest = Bp[row]; + + Bj[dest] = Aij[n].y(); + Bx[dest] = Ax[n]; + + Bp[row]++; + } + + for(int i = 0, last = 0; i <= n_row; i++){ + int temp = Bp[i]; + Bp[i] = last; + last = temp; + } + + //now Bp,Bj,Bx form a CSR representation (with possible duplicates) +} + +template< class T1, class T2 > +bool kv_pair_less(const std::pair<T1,T2>& x, const std::pair<T1,T2>& y){ + return x.first < y.first; +} + + +template<class I, class T> +void csr_sort_indices(const I n_row, + const I Ap[], + I Aj[], + T Ax[]) +{ + std::vector< std::pair<I,T> > temp; + + for(I i = 0; i < n_row; i++){ + I row_start = Ap[i]; + I row_end = Ap[i+1]; + + temp.clear(); + + for(I jj = row_start; jj < row_end; jj++){ + temp.push_back(std::make_pair(Aj[jj],Ax[jj])); + } + + std::sort(temp.begin(),temp.end(),kv_pair_less<I,T>); + + for(I jj = row_start, n = 0; jj < row_end; jj++, n++){ + Aj[jj] = temp[n].first; + Ax[jj] = temp[n].second; + } + } +} + +template <class I, class T> +void csr_sum_duplicates(const I n_row, + const I n_col, + I Ap[], + I Aj[], + T Ax[]) +{ + I nnz = 0; + I row_end = 0; + for(I i = 0; i < n_row; i++){ + I jj = row_end; + row_end = Ap[i+1]; + while( jj < row_end ){ + I j = Aj[jj]; + T x = Ax[jj]; + jj++; + while( jj < row_end && Aj[jj] == j ){ + x += Ax[jj]; + jj++; + } + Aj[nnz] = j; + Ax[nnz] = x; + nnz++; + } + Ap[i+1] = nnz; + } +} + +EIGEN_DONT_INLINE Scalar* setrand_scipy(const Coordinates& coords, const Values& vals) +{ + using namespace Eigen; + SparseMatrix<Scalar> mat(SIZE,SIZE); + mat.resizeNonZeros(coords.size()); +// std::cerr << "setrand_scipy...\n"; + coo_tocsr<Scalar>(SIZE,SIZE, coords.size(), coords, vals, mat._outerIndexPtr(), mat._innerIndexPtr(), mat._valuePtr()); +// std::cerr << "coo_tocsr ok\n"; + + csr_sort_indices(SIZE, mat._outerIndexPtr(), mat._innerIndexPtr(), mat._valuePtr()); + + csr_sum_duplicates(SIZE, SIZE, mat._outerIndexPtr(), mat._innerIndexPtr(), mat._valuePtr()); + + mat.resizeNonZeros(mat._outerIndexPtr()[SIZE]); + + return &mat.coeffRef(coords[0].x(), coords[0].y()); +} + + +#ifndef NOUBLAS +EIGEN_DONT_INLINE Scalar* setrand_ublas_mapped(const Coordinates& coords, const Values& vals) +{ + using namespace boost; + using namespace boost::numeric; + using namespace boost::numeric::ublas; + mapped_matrix<Scalar> aux(SIZE,SIZE); + for (int i=0; i<coords.size(); ++i) + { + aux(coords[i].x(), coords[i].y()) += vals[i]; + } + CHECK_MEM; + compressed_matrix<Scalar> mat(aux); + return 0;// &mat(coords[0].x(), coords[0].y()); +} +/*EIGEN_DONT_INLINE Scalar* setrand_ublas_coord(const Coordinates& coords, const Values& vals) +{ + using namespace boost; + using namespace boost::numeric; + using namespace boost::numeric::ublas; + coordinate_matrix<Scalar> aux(SIZE,SIZE); + for (int i=0; i<coords.size(); ++i) + { + aux(coords[i].x(), coords[i].y()) = vals[i]; + } + compressed_matrix<Scalar> mat(aux); + return 0;//&mat(coords[0].x(), coords[0].y()); +} +EIGEN_DONT_INLINE Scalar* setrand_ublas_compressed(const Coordinates& coords, const Values& vals) +{ + using namespace boost; + using namespace boost::numeric; + using namespace boost::numeric::ublas; + compressed_matrix<Scalar> mat(SIZE,SIZE); + for (int i=0; i<coords.size(); ++i) + { + mat(coords[i].x(), coords[i].y()) = vals[i]; + } + return 0;//&mat(coords[0].x(), coords[0].y()); +}*/ +EIGEN_DONT_INLINE Scalar* setrand_ublas_genvec(const Coordinates& coords, const Values& vals) +{ + using namespace boost; + using namespace boost::numeric; + using namespace boost::numeric::ublas; + +// ublas::vector<coordinate_vector<Scalar> > foo; + generalized_vector_of_vector<Scalar, row_major, ublas::vector<coordinate_vector<Scalar> > > aux(SIZE,SIZE); + for (int i=0; i<coords.size(); ++i) + { + aux(coords[i].x(), coords[i].y()) += vals[i]; + } + CHECK_MEM; + compressed_matrix<Scalar,row_major> mat(aux); + return 0;//&mat(coords[0].x(), coords[0].y()); +} +#endif + +#ifndef NOMTL +EIGEN_DONT_INLINE void setrand_mtl(const Coordinates& coords, const Values& vals); +#endif + |