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https://gitlab.com/libeigen/eigen.git
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add/update some benchmark files used to test/compare sparse module features
This commit is contained in:
Gael Guennebaud
parent
ecc6c43dba
commit
76fe2e1b34
@ -72,3 +72,23 @@ void eiToMtl(const EigenSparseMatrix& src, MtlSparse& dst)
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ins[it.index()][j] = it.value();
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}
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#endif
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#ifdef CSPARSE
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extern "C" {
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#include "cs.h"
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}
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void eiToCSparse(const EigenSparseMatrix& src, cs* &dst)
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{
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cs* aux = cs_spalloc (0, 0, 1, 1, 1);
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for (int j=0; j<src.cols(); ++j)
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for (EigenSparseMatrix::InnerIterator it(src.derived(), j); it; ++it)
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if (!cs_entry(aux, it.index(), j, it.value()))
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{
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std::cout << "cs_entry error\n";
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exit(2);
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}
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dst = cs_compress(aux);
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// cs_spfree(aux);
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}
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#endif
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215
bench/sparse_cholesky.cpp
Normal file
215
bench/sparse_cholesky.cpp
Normal file
@ -0,0 +1,215 @@
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#define EIGEN_TAUCS_SUPPORT
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#define EIGEN_CHOLMOD_SUPPORT
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#include <Eigen/Sparse>
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// g++ -DSIZE=10000 -DDENSITY=0.001 sparse_cholesky.cpp -I.. -DDENSEMATRI -O3 -g0 -DNDEBUG -DNBTRIES=1 -I /home/gael/Coding/LinearAlgebra/taucs_full/src/ -I/home/gael/Coding/LinearAlgebra/taucs_full/build/linux/ -L/home/gael/Coding/LinearAlgebra/taucs_full/lib/linux/ -ltaucs /home/gael/Coding/LinearAlgebra/GotoBLAS/libgoto.a -lpthread -I /home/gael/Coding/LinearAlgebra/SuiteSparse/CHOLMOD/Include/ $CHOLLIB -I /home/gael/Coding/LinearAlgebra/SuiteSparse/UFconfig/ /home/gael/Coding/LinearAlgebra/SuiteSparse/CCOLAMD/Lib/libccolamd.a /home/gael/Coding/LinearAlgebra/SuiteSparse/CHOLMOD/Lib/libcholmod.a -lmetis /home/gael/Coding/LinearAlgebra/SuiteSparse/AMD/Lib/libamd.a /home/gael/Coding/LinearAlgebra/SuiteSparse/CAMD/Lib/libcamd.a /home/gael/Coding/LinearAlgebra/SuiteSparse/CCOLAMD/Lib/libccolamd.a /home/gael/Coding/LinearAlgebra/SuiteSparse/COLAMD/Lib/libcolamd.a -llapack && ./a.out
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#define NOGMM
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#define NOMTL
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#ifndef SIZE
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#define SIZE 10
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#endif
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#ifndef DENSITY
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#define DENSITY 0.01
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#endif
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#ifndef REPEAT
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#define REPEAT 1
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#endif
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#include "BenchSparseUtil.h"
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#ifndef MINDENSITY
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#define MINDENSITY 0.0004
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#endif
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#ifndef NBTRIES
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#define NBTRIES 10
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#endif
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#define BENCH(X) \
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timer.reset(); \
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for (int _j=0; _j<NBTRIES; ++_j) { \
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timer.start(); \
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for (int _k=0; _k<REPEAT; ++_k) { \
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X \
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} timer.stop(); }
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// typedef SparseMatrix<Scalar,Upper> EigenSparseTriMatrix;
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typedef SparseMatrix<Scalar,SelfAdjoint|Lower> EigenSparseSelfAdjointMatrix;
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void fillSpdMatrix(float density, int rows, int cols, EigenSparseSelfAdjointMatrix& dst)
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{
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dst.startFill(rows*cols*density);
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for(int j = 0; j < cols; j++)
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{
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dst.fill(j,j) = ei_random<Scalar>(10,20);
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for(int i = j+1; i < rows; i++)
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{
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Scalar v = (ei_random<float>(0,1) < density) ? ei_random<Scalar>() : 0;
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if (v!=0)
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dst.fill(i,j) = v;
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}
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}
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dst.endFill();
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}
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#include <Eigen/Cholesky>
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template<int Backend>
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void doEigen(const char* name, const EigenSparseSelfAdjointMatrix& sm1, int flags = 0)
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{
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std::cout << name << "..." << std::flush;
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BenchTimer timer;
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timer.start();
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SparseLLT<EigenSparseSelfAdjointMatrix,Backend> chol(sm1, flags);
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timer.stop();
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std::cout << ":\t" << timer.value() << endl;
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std::cout << " nnz: " << sm1.nonZeros() << " => " << chol.matrixL().nonZeros() << "\n";
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//std::cout << "sparse\n" << chol.matrixL() << "%\n";
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}
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int main(int argc, char *argv[])
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{
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int rows = SIZE;
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int cols = SIZE;
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float density = DENSITY;
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BenchTimer timer;
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VectorXf b = VectorXf::Random(cols);
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VectorXf x = VectorXf::Random(cols);
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bool densedone = false;
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//for (float density = DENSITY; density>=MINDENSITY; density*=0.5)
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// float density = 0.5;
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{
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EigenSparseSelfAdjointMatrix sm1(rows, cols);
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std::cout << "Generate sparse matrix (might take a while)...\n";
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fillSpdMatrix(density, rows, cols, sm1);
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std::cout << "DONE\n\n";
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// dense matrices
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#ifdef DENSEMATRIX
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if (!densedone)
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{
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densedone = true;
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std::cout << "Eigen Dense\t" << density*100 << "%\n";
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DenseMatrix m1(rows,cols);
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eiToDense(sm1, m1);
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m1 = (m1 + m1.transpose()).eval();
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m1.diagonal() *= 0.5;
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// BENCH(LLT<DenseMatrix> chol(m1);)
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// std::cout << "dense:\t" << timer.value() << endl;
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BenchTimer timer;
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timer.start();
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LLT<DenseMatrix> chol(m1);
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timer.stop();
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std::cout << "dense:\t" << timer.value() << endl;
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int count = 0;
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for (int j=0; j<cols; ++j)
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for (int i=j; i<rows; ++i)
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if (!ei_isMuchSmallerThan(ei_abs(chol.matrixL()(i,j)), 0.1))
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count++;
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std::cout << "dense: " << "nnz = " << count << "\n";
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std::cout << "dense:\n" << m1 << "\n\n" << chol.matrixL() << endl;
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}
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#endif
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// eigen sparse matrices
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doEigen<Eigen::DefaultBackend>("Eigen/Sparse", sm1, Eigen::IncompleteFactorization);
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#ifdef EIGEN_CHOLMOD_SUPPORT
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doEigen<Eigen::Cholmod>("Eigen/Cholmod", sm1, Eigen::IncompleteFactorization);
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#endif
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#ifdef EIGEN_TAUCS_SUPPORT
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doEigen<Eigen::Taucs>("Eigen/Taucs", sm1, Eigen::IncompleteFactorization);
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#endif
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#if 0
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// TAUCS
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{
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taucs_ccs_matrix A = sm1.asTaucsMatrix();
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//BENCH(taucs_ccs_matrix* chol = taucs_ccs_factor_llt(&A, 0, 0);)
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// BENCH(taucs_supernodal_factor_to_ccs(taucs_ccs_factor_llt_ll(&A));)
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// std::cout << "taucs:\t" << timer.value() << endl;
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taucs_ccs_matrix* chol = taucs_ccs_factor_llt(&A, 0, 0);
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for (int j=0; j<cols; ++j)
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{
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for (int i=chol->colptr[j]; i<chol->colptr[j+1]; ++i)
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std::cout << chol->values.d[i] << " ";
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}
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}
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// CHOLMOD
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#ifdef EIGEN_CHOLMOD_SUPPORT
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{
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cholmod_common c;
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cholmod_start (&c);
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cholmod_sparse A;
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cholmod_factor *L;
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A = sm1.asCholmodMatrix();
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BenchTimer timer;
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// timer.reset();
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timer.start();
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std::vector<int> perm(cols);
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// std::vector<int> set(ncols);
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for (int i=0; i<cols; ++i)
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perm[i] = i;
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// c.nmethods = 1;
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// c.method[0] = 1;
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c.nmethods = 1;
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c.method [0].ordering = CHOLMOD_NATURAL;
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c.postorder = 0;
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c.final_ll = 1;
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L = cholmod_analyze_p(&A, &perm[0], &perm[0], cols, &c);
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timer.stop();
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std::cout << "cholmod/analyze:\t" << timer.value() << endl;
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timer.reset();
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timer.start();
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cholmod_factorize(&A, L, &c);
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timer.stop();
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std::cout << "cholmod/factorize:\t" << timer.value() << endl;
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cholmod_sparse* cholmat = cholmod_factor_to_sparse(L, &c);
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cholmod_print_factor(L, "Factors", &c);
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cholmod_print_sparse(cholmat, "Chol", &c);
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cholmod_write_sparse(stdout, cholmat, 0, 0, &c);
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//
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// cholmod_print_sparse(&A, "A", &c);
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// cholmod_write_sparse(stdout, &A, 0, 0, &c);
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// for (int j=0; j<cols; ++j)
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// {
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// for (int i=chol->colptr[j]; i<chol->colptr[j+1]; ++i)
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// std::cout << chol->values.s[i] << " ";
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// }
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}
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#endif
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#endif
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}
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return 0;
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}
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112
bench/sparse_lu.cpp
Normal file
112
bench/sparse_lu.cpp
Normal file
@ -0,0 +1,112 @@
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// g++ -I.. sparse_lu.cpp -O3 -g0 -I /usr/include/superlu/ -lsuperlu -lgfortran -DSIZE=1000 -DDENSITY=.05 && ./a.out
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// #define EIGEN_TAUCS_SUPPORT
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// #define EIGEN_CHOLMOD_SUPPORT
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#define EIGEN_SUPERLU_SUPPORT
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#include <Eigen/Sparse>
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#define NOGMM
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#define NOMTL
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#ifndef SIZE
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#define SIZE 10
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#endif
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#ifndef DENSITY
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#define DENSITY 0.01
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#endif
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#ifndef REPEAT
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#define REPEAT 1
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#endif
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#include "BenchSparseUtil.h"
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#ifndef MINDENSITY
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#define MINDENSITY 0.0004
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#endif
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#ifndef NBTRIES
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#define NBTRIES 10
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#endif
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#define BENCH(X) \
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timer.reset(); \
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for (int _j=0; _j<NBTRIES; ++_j) { \
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timer.start(); \
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for (int _k=0; _k<REPEAT; ++_k) { \
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X \
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} timer.stop(); }
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typedef Matrix<Scalar,Dynamic,1> VectorX;
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#include <Eigen/LU>
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int main(int argc, char *argv[])
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{
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int rows = SIZE;
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int cols = SIZE;
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float density = DENSITY;
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BenchTimer timer;
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VectorX b = VectorX::Random(cols);
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VectorX x = VectorX::Random(cols);
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bool densedone = false;
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//for (float density = DENSITY; density>=MINDENSITY; density*=0.5)
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// float density = 0.5;
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{
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EigenSparseMatrix sm1(rows, cols);
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fillMatrix(density, rows, cols, sm1);
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// dense matrices
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#ifdef DENSEMATRIX
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if (!densedone)
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{
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densedone = true;
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std::cout << "Eigen Dense\t" << density*100 << "%\n";
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DenseMatrix m1(rows,cols);
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eiToDense(sm1, m1);
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BenchTimer timer;
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timer.start();
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LU<DenseMatrix> lu(m1);
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timer.stop();
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std::cout << "Eigen/dense:\t" << timer.value() << endl;
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timer.reset();
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timer.start();
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lu.solve(b,&x);
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timer.stop();
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std::cout << " solve:\t" << timer.value() << endl;
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// std::cout << b.transpose() << "\n";
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std::cout << x.transpose() << "\n";
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}
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#endif
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// eigen sparse matrices
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{
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x.setZero();
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BenchTimer timer;
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timer.start();
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SparseLU<EigenSparseMatrix,SuperLU> lu(sm1);
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timer.stop();
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std::cout << "Eigen/SuperLU:\t" << timer.value() << endl;
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timer.reset();
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timer.start();
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lu.solve(b,&x);
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timer.stop();
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std::cout << " solve:\t" << timer.value() << endl;
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std::cout << x.transpose() << "\n";
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}
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}
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return 0;
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}
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@ -1,8 +1,8 @@
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//g++ -O3 -g0 -DNDEBUG sparse_product.cpp -I.. -I/home/gael/Coding/LinearAlgebra/mtl4/ -DDENSITY=0.005 -DSIZE=10000 && ./a.out
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//g++ -O3 -g0 -DNDEBUG sparse_product.cpp -I.. -I/home/gael/Coding/LinearAlgebra/mtl4/ -DDENSITY=0.05 -DSIZE=2000 && ./a.out
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// -DNOGMM -DNOMTL
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// -DNOGMM -DNOMTL -DCSPARSE
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// -I /home/gael/Coding/LinearAlgebra/CSparse/Include/ /home/gael/Coding/LinearAlgebra/CSparse/Lib/libcsparse.a
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#ifndef SIZE
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#define SIZE 10000
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#endif
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@ -33,6 +33,22 @@
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X \
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} timer.stop(); }
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#ifdef CSPARSE
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cs* cs_sorted_multiply(const cs* a, const cs* b)
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{
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cs* A = cs_transpose (a, 1) ;
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cs* B = cs_transpose (b, 1) ;
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cs* D = cs_multiply (B,A) ; /* D = B'*A' */
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cs_spfree (A) ;
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cs_spfree (B) ;
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cs_dropzeros (D) ; /* drop zeros from D */
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cs* C = cs_transpose (D, 1) ; /* C = D', so that C is sorted */
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cs_spfree (D) ;
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return C;
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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 rows = SIZE;
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@ -87,13 +103,15 @@ int main(int argc, char *argv[])
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// eigen sparse matrices
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{
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std::cout << "Eigen sparse\t" << density*100 << "%\n";
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std::cout << "Eigen sparse\t" << sm1.nonZeros()/float(sm1.rows()*sm1.cols())*100 << "% * "
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<< sm2.nonZeros()/float(sm2.rows()*sm2.cols())*100 << "%\n";
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// timer.reset();
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// timer.start();
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BENCH(for (int k=0; k<REPEAT; ++k) sm3 = sm1 * sm2;)
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// timer.stop();
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std::cout << " a * b:\t" << timer.value() << endl;
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// std::cout << sm3 << "\n";
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timer.reset();
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timer.start();
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@ -120,6 +138,32 @@ int main(int argc, char *argv[])
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std::cout << " a * b' :\t" << timer.value() << 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 \t" << density*100 << "%\n";
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cs *m1, *m2, *m3;
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eiToCSparse(sm1, m1);
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eiToCSparse(sm2, m2);
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timer.reset();
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timer.start();
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for (int k=0; k<REPEAT; ++k)
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{
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m3 = cs_sorted_multiply(m1, m2);
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if (!m3)
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{
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std::cerr << "cs_multiply failed\n";
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// break;
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}
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// cs_print(m3, 0);
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cs_spfree(m3);
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}
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timer.stop();
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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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// GMM++
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#ifndef NOGMM
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{
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104
bench/sparse_transpose.cpp
Normal file
104
bench/sparse_transpose.cpp
Normal file
@ -0,0 +1,104 @@
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//g++ -O3 -g0 -DNDEBUG sparse_transpose.cpp -I.. -I/home/gael/Coding/LinearAlgebra/mtl4/ -DDENSITY=0.005 -DSIZE=10000 && ./a.out
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// -DNOGMM -DNOMTL
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// -DCSPARSE -I /home/gael/Coding/LinearAlgebra/CSparse/Include/ /home/gael/Coding/LinearAlgebra/CSparse/Lib/libcsparse.a
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#ifndef SIZE
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#define SIZE 10000
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#endif
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#ifndef DENSITY
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#define DENSITY 0.01
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#endif
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#ifndef REPEAT
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#define REPEAT 1
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#endif
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#include "BenchSparseUtil.h"
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#ifndef MINDENSITY
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#define MINDENSITY 0.0004
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#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(); }
|
||||
|
||||
int main(int argc, char *argv[])
|
||||
{
|
||||
int rows = SIZE;
|
||||
int cols = SIZE;
|
||||
float density = DENSITY;
|
||||
|
||||
EigenSparseMatrix sm1(rows,cols), sm3(rows,cols);
|
||||
|
||||
BenchTimer timer;
|
||||
for (float density = DENSITY; density>=MINDENSITY; density*=0.5)
|
||||
{
|
||||
fillMatrix(density, rows, cols, sm1);
|
||||
|
||||
// dense matrices
|
||||
#ifdef DENSEMATRIX
|
||||
{
|
||||
DenseMatrix m1(rows,cols), m3(rows,cols);
|
||||
eiToDense(sm1, m1);
|
||||
BENCH(for (int k=0; k<REPEAT; ++k) m3 = m1.transpose();)
|
||||
std::cout << " Eigen dense:\t" << timer.value() << endl;
|
||||
}
|
||||
#endif
|
||||
|
||||
std::cout << "Non zeros: " << sm1.nonZeros()/float(sm1.rows()*sm1.cols())*100 << "%\n";
|
||||
|
||||
// eigen sparse matrices
|
||||
{
|
||||
BENCH(for (int k=0; k<REPEAT; ++k) sm3 = sm1.transpose();)
|
||||
std::cout << " Eigen:\t" << timer.value() << endl;
|
||||
}
|
||||
|
||||
// CSparse
|
||||
#ifdef CSPARSE
|
||||
{
|
||||
cs *m1, *m3;
|
||||
eiToCSparse(sm1, m1);
|
||||
|
||||
BENCH(for (int k=0; k<REPEAT; ++k) { m3 = cs_transpose(m1,1); cs_spfree(m3);})
|
||||
std::cout << " CSparse:\t" << timer.value() << endl;
|
||||
}
|
||||
#endif
|
||||
|
||||
// GMM++
|
||||
#ifndef NOGMM
|
||||
{
|
||||
GmmDynSparse gmmT3(rows,cols);
|
||||
GmmSparse m1(rows,cols), m3(rows,cols);
|
||||
eiToGmm(sm1, m1);
|
||||
BENCH(for (int k=0; k<REPEAT; ++k) gmm::copy(gmm::transposed(m1),m3);)
|
||||
std::cout << " GMM:\t\t" << timer.value() << endl;
|
||||
}
|
||||
#endif
|
||||
|
||||
// MTL4
|
||||
#ifndef NOMTL
|
||||
{
|
||||
MtlSparse m1(rows,cols), m3(rows,cols);
|
||||
eiToMtl(sm1, m1);
|
||||
BENCH(for (int k=0; k<REPEAT; ++k) m3 = trans(m1);)
|
||||
std::cout << " MTL4:\t\t" << timer.value() << endl;
|
||||
}
|
||||
#endif
|
||||
|
||||
std::cout << "\n\n";
|
||||
}
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
||||
@ -2,6 +2,7 @@
|
||||
//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
|
||||
@ -60,8 +61,9 @@ int main(int argc, char *argv[])
|
||||
BenchTimer timer;
|
||||
#if 1
|
||||
EigenSparseTriMatrix sm1(rows,cols);
|
||||
VectorXf b = VectorXf::Random(cols);
|
||||
VectorXf x = VectorXf::Random(cols);
|
||||
typedef Matrix<Scalar,Dynamic,1> DenseVector;
|
||||
DenseVector b = DenseVector::Random(cols);
|
||||
DenseVector x = DenseVector::Random(cols);
|
||||
|
||||
bool densedone = false;
|
||||
|
||||
@ -81,13 +83,13 @@ int main(int argc, char *argv[])
|
||||
eiToDense(sm1, m1);
|
||||
m2 = m1;
|
||||
|
||||
BENCH(x = m1.marked<Upper>().inverseProduct(b);)
|
||||
BENCH(x = m1.marked<Upper>().solveTriangular(b);)
|
||||
std::cout << " colmajor^-1 * b:\t" << timer.value() << endl;
|
||||
std::cerr << x.transpose() << "\n";
|
||||
// std::cerr << x.transpose() << "\n";
|
||||
|
||||
BENCH(x = m2.marked<Upper>().inverseProduct(b);)
|
||||
BENCH(x = m2.marked<Upper>().solveTriangular(b);)
|
||||
std::cout << " rowmajor^-1 * b:\t" << timer.value() << endl;
|
||||
std::cerr << x.transpose() << "\n";
|
||||
// std::cerr << x.transpose() << "\n";
|
||||
}
|
||||
#endif
|
||||
|
||||
@ -96,13 +98,13 @@ int main(int argc, char *argv[])
|
||||
std::cout << "Eigen sparse\t" << density*100 << "%\n";
|
||||
EigenSparseTriMatrixRow sm2 = sm1;
|
||||
|
||||
BENCH(x = sm1.inverseProduct(b);)
|
||||
BENCH(x = sm1.solveTriangular(b);)
|
||||
std::cout << " colmajor^-1 * b:\t" << timer.value() << endl;
|
||||
std::cerr << x.transpose() << "\n";
|
||||
// std::cerr << x.transpose() << "\n";
|
||||
|
||||
BENCH(x = sm2.inverseProduct(b);)
|
||||
BENCH(x = sm2.solveTriangular(b);)
|
||||
std::cout << " rowmajor^-1 * b:\t" << timer.value() << endl;
|
||||
std::cerr << x.transpose() << "\n";
|
||||
// std::cerr << x.transpose() << "\n";
|
||||
|
||||
// x = b;
|
||||
// BENCH(sm1.inverseProductInPlace(x);)
|
||||
@ -115,6 +117,18 @@ int main(int argc, char *argv[])
|
||||
// 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
|
||||
{
|
||||
@ -130,13 +144,13 @@ int main(int argc, char *argv[])
|
||||
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";
|
||||
// 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";
|
||||
// std::cerr << Map<Matrix<Scalar,Dynamic,1> >(&gmmX[0], cols).transpose() << "\n";
|
||||
}
|
||||
#endif
|
||||
|
||||
@ -162,7 +176,7 @@ int main(int argc, char *argv[])
|
||||
#endif
|
||||
|
||||
|
||||
|
||||
std::cout << "\n\n";
|
||||
}
|
||||
#endif
|
||||
|
||||
@ -199,8 +213,6 @@ int main(int argc, char *argv[])
|
||||
}
|
||||
#endif
|
||||
|
||||
std::cout << "\n\n";
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user