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add a spmv mini becnhmark for Eigen, GMM++, ublas, mtl4, and oski
This commit is contained in:
Gael Guennebaud
parent
b4fe53f561
commit
b284bb8bba
@ -122,6 +122,8 @@ void eiToCSparse(const EigenSparseMatrix& src, cs* &dst)
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#include <boost/numeric/ublas/vector_sparse.hpp>
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#include <boost/numeric/ublas/matrix_sparse.hpp>
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#include <boost/numeric/ublas/vector_of_vector.hpp>
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#include <boost/numeric/ublas/operation.hpp>
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// #include <boost/numeric/ublas/sparse_prod.hpp>
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// using namespace boost;
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// using namespace boost::numeric;
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233
bench/spmv.cpp
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233
bench/spmv.cpp
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@ -0,0 +1,233 @@
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//g++-4.4 -DNOMTL -Wl,-rpath /usr/local/lib/oski -L /usr/local/lib/oski/ -l oski -l oski_util -l oski_util_Tid -DOSKI -I ~/Coding/LinearAlgebra/mtl4/ spmv.cpp -I .. -O2 -DNDEBUG -lrt -lm -l oski_mat_CSC_Tid -loskilt && ./a.out r200000 c200000 n100 t1 p1
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#define SCALAR double
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#include <iostream>
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#include <algorithm>
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#include "BenchTimer.h"
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#include "BenchSparseUtil.h"
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#define SPMV_BENCH(CODE) BENCH(t,tries,repeats,CODE);
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// #ifdef MKL
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//
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// #include "mkl_types.h"
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// #include "mkl_spblas.h"
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//
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// template<typename Lhs,typename Rhs,typename Res>
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// void mkl_multiply(const Lhs& lhs, const Rhs& rhs, Res& res)
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// {
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// char n = 'N';
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// float alpha = 1;
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// char matdescra[6];
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// matdescra[0] = 'G';
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// matdescra[1] = 0;
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// matdescra[2] = 0;
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// matdescra[3] = 'C';
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// mkl_scscmm(&n, lhs.rows(), rhs.cols(), lhs.cols(), &alpha, matdescra,
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// lhs._valuePtr(), lhs._innerIndexPtr(), lhs.outerIndexPtr(),
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// pntre, b, &ldb, &beta, c, &ldc);
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// // mkl_somatcopy('C', 'T', lhs.rows(), lhs.cols(), 1,
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// // lhs._valuePtr(), lhs.rows(), DST, dst_stride);
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// }
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//
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// #endif
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int main(int argc, char *argv[])
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{
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int size = 10000;
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int rows = size;
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int cols = size;
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int nnzPerCol = 40;
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int tries = 2;
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int repeats = 2;
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bool need_help = false;
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for(int i = 1; i < argc; i++)
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{
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if(argv[i][0] == 'r')
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{
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rows = atoi(argv[i]+1);
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}
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else if(argv[i][0] == 'c')
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{
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cols = atoi(argv[i]+1);
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}
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else if(argv[i][0] == 'n')
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{
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nnzPerCol = atoi(argv[i]+1);
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}
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else if(argv[i][0] == 't')
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{
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tries = atoi(argv[i]+1);
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}
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else if(argv[i][0] == 'p')
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{
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repeats = atoi(argv[i]+1);
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}
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else
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{
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need_help = true;
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}
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}
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if(need_help)
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{
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std::cout << argv[0] << " r<nb rows> c<nb columns> n<non zeros per column> t<nb tries> p<nb repeats>\n";
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return 1;
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}
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std::cout << "SpMV " << rows << " x " << cols << " with " << nnzPerCol << " non zeros per column. (" << repeats << " repeats, and " << tries << " tries)\n\n";
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EigenSparseMatrix sm(rows,cols);
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DenseVector dv(cols), res(rows);
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dv.setRandom();
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BenchTimer t;
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while (nnzPerCol>=4)
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{
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std::cout << "nnz: " << nnzPerCol << "\n";
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sm.setZero();
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fillMatrix2(nnzPerCol, rows, cols, sm);
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// dense matrices
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#ifdef DENSEMATRIX
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{
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DenseMatrix dm(rows,cols), (rows,cols);
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eiToDense(sm, dm);
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SPMV_BENCH(res = dm * sm);
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std::cout << "Dense " << t.value()/repeats << "\t";
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SPMV_BENCHres = dm.transpose() * sm);
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std::cout << t.value()/repeats << endl;
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}
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#endif
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// eigen sparse matrices
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{
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SPMV_BENCH(res.noalias() += sm * dv; )
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std::cout << "Eigen " << t.value()/repeats << "\t";
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SPMV_BENCH(res.noalias() += sm.transpose() * dv; )
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std::cout << t.value()/repeats << endl;
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}
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// CSparse
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#ifdef CSPARSE
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{
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std::cout << "CSparse \n";
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cs *csm;
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eiToCSparse(sm, csm);
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// BENCH();
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// timer.stop();
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// std::cout << " a * b:\t" << timer.value() << endl;
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// BENCH( { m3 = cs_sorted_multiply2(m1, m2); cs_spfree(m3); } );
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// std::cout << " a * b:\t" << timer.value() << endl;
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}
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#endif
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#ifdef OSKI
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{
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oski_matrix_t om;
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oski_vecview_t ov, ores;
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oski_Init();
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om = oski_CreateMatCSC(sm._outerIndexPtr(), sm._innerIndexPtr(), sm._valuePtr(), rows, cols,
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SHARE_INPUTMAT, 1, INDEX_ZERO_BASED);
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ov = oski_CreateVecView(dv.data(), cols, STRIDE_UNIT);
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ores = oski_CreateVecView(res.data(), rows, STRIDE_UNIT);
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SPMV_BENCH( oski_MatMult(om, OP_NORMAL, 1, ov, 0, ores) );
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std::cout << "OSKI " << t.value()/repeats << "\t";
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SPMV_BENCH( oski_MatMult(om, OP_TRANS, 1, ov, 0, ores) );
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std::cout << t.value()/repeats << "\n";
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// tune
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t.reset();
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t.start();
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oski_SetHintMatMult(om, OP_NORMAL, 1.0, SYMBOLIC_VEC, 0.0, SYMBOLIC_VEC, ALWAYS_TUNE_AGGRESSIVELY);
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oski_TuneMat(om);
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t.stop();
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double tuning = t.value();
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SPMV_BENCH( oski_MatMult(om, OP_NORMAL, 1, ov, 0, ores) );
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std::cout << "OSKI tuned " << t.value()/repeats << "\t";
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SPMV_BENCH( oski_MatMult(om, OP_TRANS, 1, ov, 0, ores) );
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std::cout << t.value()/repeats << "\t(" << tuning << ")\n";
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oski_DestroyMat(om);
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oski_DestroyVecView(ov);
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oski_DestroyVecView(ores);
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oski_Close();
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}
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#endif
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#ifndef NOUBLAS
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{
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using namespace boost::numeric;
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UblasMatrix um(rows,cols);
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eiToUblas(sm, um);
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boost::numeric::ublas::vector<Scalar> uv(cols), ures(rows);
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Map<Matrix<Scalar,Dynamic,1> >(&uv[0], cols) = dv;
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Map<Matrix<Scalar,Dynamic,1> >(&ures[0], rows) = res;
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SPMV_BENCH(ublas::axpy_prod(um, uv, ures, true));
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std::cout << "ublas " << t.value()/repeats << "\t";
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SPMV_BENCH(ublas::axpy_prod(boost::numeric::ublas::trans(um), uv, ures, true));
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std::cout << t.value()/repeats << endl;
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}
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#endif
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// GMM++
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#ifndef NOGMM
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{
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GmmSparse gm(rows,cols);
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eiToGmm(sm, gm);
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std::vector<Scalar> gv(cols), gres(rows);
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Map<Matrix<Scalar,Dynamic,1> >(&gv[0], cols) = dv;
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Map<Matrix<Scalar,Dynamic,1> >(&gres[0], rows) = res;
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SPMV_BENCH(gmm::mult(gm, gv, gres));
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std::cout << "GMM++ " << t.value()/repeats << "\t";
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SPMV_BENCH(gmm::mult(gmm::transposed(gm), gv, gres));
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std::cout << t.value()/repeats << endl;
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}
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#endif
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// MTL4
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#ifndef NOMTL
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{
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MtlSparse mm(rows,cols);
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eiToMtl(sm, mm);
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mtl::dense_vector<Scalar> mv(cols, 1.0);
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mtl::dense_vector<Scalar> mres(rows, 1.0);
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SPMV_BENCH(mres = mm * mv);
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std::cout << "MTL4 " << t.value()/repeats << "\t";
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SPMV_BENCH(mres = trans(mm) * mv);
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std::cout << t.value()/repeats << endl;
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}
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#endif
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std::cout << "\n";
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if(nnzPerCol==1)
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break;
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nnzPerCol -= nnzPerCol/2;
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}
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return 0;
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}
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