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benchEigenSolver.cpp
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Thu, Feb 20, 22:54

benchEigenSolver.cpp
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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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