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// g++ -DNDEBUG -O3 -I.. benchEigenSolver.cpp -o benchEigenSolver && ./benchEigenSolver
// options:
// -DBENCH_GMM
// -DBENCH_GSL -lgsl /usr/lib/libcblas.so.3
// -DEIGEN_DONT_VECTORIZE
// -msse2
// -DREPEAT=100
// -DTRIES=10
// -DSCALAR=double
#include <iostream>
#include <Eigen/Core>
#include <Eigen/QR>
#include <bench/BenchUtil.h>
using namespace Eigen;
#ifndef REPEAT
#define REPEAT 1000
#endif
#ifndef TRIES
#define TRIES 4
#endif
#ifndef SCALAR
#define SCALAR float
#endif
typedef SCALAR Scalar;
template <typename MatrixType> __attribute__ ((noinline)) void benchEigenSolver(const MatrixType& m) { int rows = m.rows(); int cols = m.cols();
int stdRepeats = std::max(1,int((REPEAT*1000)/(rows*rows*sqrt(rows)))); int saRepeats = stdRepeats * 4;
typedef typename MatrixType::Scalar Scalar; typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> SquareMatrixType;
MatrixType a = MatrixType::Random(rows,cols); SquareMatrixType covMat = a * a.adjoint();
BenchTimer timerSa, timerStd;
Scalar acc = 0; int r = internal::random<int>(0,covMat.rows()-1); int c = internal::random<int>(0,covMat.cols()-1); { SelfAdjointEigenSolver<SquareMatrixType> ei(covMat); for (int t=0; t<TRIES; ++t) { timerSa.start(); for (int k=0; k<saRepeats; ++k) { ei.compute(covMat); acc += ei.eigenvectors().coeff(r,c); } timerSa.stop(); } }
{ EigenSolver<SquareMatrixType> ei(covMat); for (int t=0; t<TRIES; ++t) { timerStd.start(); for (int k=0; k<stdRepeats; ++k) { ei.compute(covMat); acc += ei.eigenvectors().coeff(r,c); } timerStd.stop(); } }
if (MatrixType::RowsAtCompileTime==Dynamic) std::cout << "dyn "; else std::cout << "fixed "; std::cout << covMat.rows() << " \t" << timerSa.value() * REPEAT / saRepeats << "s \t" << timerStd.value() * REPEAT / stdRepeats << "s";
#ifdef BENCH_GMM
if (MatrixType::RowsAtCompileTime==Dynamic) { timerSa.reset(); timerStd.reset();
gmm::dense_matrix<Scalar> gmmCovMat(covMat.rows(),covMat.cols()); gmm::dense_matrix<Scalar> eigvect(covMat.rows(),covMat.cols()); std::vector<Scalar> eigval(covMat.rows()); eiToGmm(covMat, gmmCovMat); for (int t=0; t<TRIES; ++t) { timerSa.start(); for (int k=0; k<saRepeats; ++k) { gmm::symmetric_qr_algorithm(gmmCovMat, eigval, eigvect); acc += eigvect(r,c); } timerSa.stop(); } // the non-selfadjoint solver does not compute the eigen vectors
// for (int t=0; t<TRIES; ++t)
// {
// timerStd.start();
// for (int k=0; k<stdRepeats; ++k)
// {
// gmm::implicit_qr_algorithm(gmmCovMat, eigval, eigvect);
// acc += eigvect(r,c);
// }
// timerStd.stop();
// }
std::cout << " | \t" << timerSa.value() * REPEAT / saRepeats << "s" << /*timerStd.value() * REPEAT / stdRepeats << "s"*/ " na "; } #endif
#ifdef BENCH_GSL
if (MatrixType::RowsAtCompileTime==Dynamic) { timerSa.reset(); timerStd.reset();
gsl_matrix* gslCovMat = gsl_matrix_alloc(covMat.rows(),covMat.cols()); gsl_matrix* gslCopy = gsl_matrix_alloc(covMat.rows(),covMat.cols()); gsl_matrix* eigvect = gsl_matrix_alloc(covMat.rows(),covMat.cols()); gsl_vector* eigval = gsl_vector_alloc(covMat.rows()); gsl_eigen_symmv_workspace* eisymm = gsl_eigen_symmv_alloc(covMat.rows()); gsl_matrix_complex* eigvectz = gsl_matrix_complex_alloc(covMat.rows(),covMat.cols()); gsl_vector_complex* eigvalz = gsl_vector_complex_alloc(covMat.rows()); gsl_eigen_nonsymmv_workspace* einonsymm = gsl_eigen_nonsymmv_alloc(covMat.rows()); eiToGsl(covMat, &gslCovMat); for (int t=0; t<TRIES; ++t) { timerSa.start(); for (int k=0; k<saRepeats; ++k) { gsl_matrix_memcpy(gslCopy,gslCovMat); gsl_eigen_symmv(gslCopy, eigval, eigvect, eisymm); acc += gsl_matrix_get(eigvect,r,c); } timerSa.stop(); } for (int t=0; t<TRIES; ++t) { timerStd.start(); for (int k=0; k<stdRepeats; ++k) { gsl_matrix_memcpy(gslCopy,gslCovMat); gsl_eigen_nonsymmv(gslCopy, eigvalz, eigvectz, einonsymm); acc += GSL_REAL(gsl_matrix_complex_get(eigvectz,r,c)); } timerStd.stop(); }
std::cout << " | \t" << timerSa.value() * REPEAT / saRepeats << "s \t" << timerStd.value() * REPEAT / stdRepeats << "s";
gsl_matrix_free(gslCovMat); gsl_vector_free(gslCopy); gsl_matrix_free(eigvect); gsl_vector_free(eigval); gsl_matrix_complex_free(eigvectz); gsl_vector_complex_free(eigvalz); gsl_eigen_symmv_free(eisymm); gsl_eigen_nonsymmv_free(einonsymm); } #endif
std::cout << "\n"; // make sure the compiler does not optimize too much
if (acc==123) std::cout << acc; }
int main(int argc, char* argv[]) { const int dynsizes[] = {4,6,8,12,16,24,32,64,128,256,512,0}; std::cout << "size selfadjoint generic"; #ifdef BENCH_GMM
std::cout << " GMM++ "; #endif
#ifdef BENCH_GSL
std::cout << " GSL (double + ATLAS) "; #endif
std::cout << "\n"; for (uint i=0; dynsizes[i]>0; ++i) benchEigenSolver(Matrix<Scalar,Dynamic,Dynamic>(dynsizes[i],dynsizes[i]));
benchEigenSolver(Matrix<Scalar,2,2>()); benchEigenSolver(Matrix<Scalar,3,3>()); benchEigenSolver(Matrix<Scalar,4,4>()); benchEigenSolver(Matrix<Scalar,6,6>()); benchEigenSolver(Matrix<Scalar,8,8>()); benchEigenSolver(Matrix<Scalar,12,12>()); benchEigenSolver(Matrix<Scalar,16,16>()); return 0; }
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