mirror of
https://gitlab.com/libeigen/eigen.git
synced 2026-04-10 11:34:33 +08:00
Clang-format tests, examples, libraries, benchmarks, etc.
This commit is contained in:
Antonio Sánchez
committed by
Rasmus Munk Larsen
Rasmus Munk Larsen
parent
3252ecc7a4
commit
46e9cdb7fe
@@ -31,34 +31,31 @@ using namespace Eigen;
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typedef SCALAR Scalar;
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template <typename MatrixType>
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__attribute__ ((noinline)) void benchEigenSolver(const MatrixType& m)
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{
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__attribute__((noinline)) void benchEigenSolver(const MatrixType& m) {
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int rows = m.rows();
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int cols = m.cols();
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int stdRepeats = std::max(1,int((REPEAT*1000)/(rows*rows*sqrt(rows))));
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int stdRepeats = std::max(1, int((REPEAT * 1000) / (rows * rows * sqrt(rows))));
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int saRepeats = stdRepeats * 4;
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typedef typename MatrixType::Scalar Scalar;
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typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> SquareMatrixType;
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MatrixType a = MatrixType::Random(rows,cols);
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SquareMatrixType covMat = a * a.adjoint();
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MatrixType a = MatrixType::Random(rows, cols);
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SquareMatrixType covMat = a * a.adjoint();
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BenchTimer timerSa, timerStd;
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Scalar acc = 0;
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int r = internal::random<int>(0,covMat.rows()-1);
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int c = internal::random<int>(0,covMat.cols()-1);
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int r = internal::random<int>(0, covMat.rows() - 1);
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int c = internal::random<int>(0, covMat.cols() - 1);
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{
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SelfAdjointEigenSolver<SquareMatrixType> ei(covMat);
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for (int t=0; t<TRIES; ++t)
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{
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for (int t = 0; t < TRIES; ++t) {
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timerSa.start();
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for (int k=0; k<saRepeats; ++k)
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{
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for (int k = 0; k < saRepeats; ++k) {
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ei.compute(covMat);
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acc += ei.eigenvectors().coeff(r,c);
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acc += ei.eigenvectors().coeff(r, c);
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}
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timerSa.stop();
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}
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@@ -66,107 +63,94 @@ __attribute__ ((noinline)) void benchEigenSolver(const MatrixType& m)
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{
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EigenSolver<SquareMatrixType> ei(covMat);
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for (int t=0; t<TRIES; ++t)
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{
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for (int t = 0; t < TRIES; ++t) {
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timerStd.start();
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for (int k=0; k<stdRepeats; ++k)
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{
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for (int k = 0; k < stdRepeats; ++k) {
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ei.compute(covMat);
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acc += ei.eigenvectors().coeff(r,c);
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acc += ei.eigenvectors().coeff(r, c);
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}
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timerStd.stop();
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}
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}
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if (MatrixType::RowsAtCompileTime==Dynamic)
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if (MatrixType::RowsAtCompileTime == Dynamic)
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std::cout << "dyn ";
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else
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std::cout << "fixed ";
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std::cout << covMat.rows() << " \t"
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<< timerSa.value() * REPEAT / saRepeats << "s \t"
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std::cout << covMat.rows() << " \t" << timerSa.value() * REPEAT / saRepeats << "s \t"
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<< timerStd.value() * REPEAT / stdRepeats << "s";
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#ifdef BENCH_GMM
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if (MatrixType::RowsAtCompileTime==Dynamic)
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{
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#ifdef BENCH_GMM
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if (MatrixType::RowsAtCompileTime == Dynamic) {
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timerSa.reset();
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timerStd.reset();
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gmm::dense_matrix<Scalar> gmmCovMat(covMat.rows(),covMat.cols());
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gmm::dense_matrix<Scalar> eigvect(covMat.rows(),covMat.cols());
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gmm::dense_matrix<Scalar> gmmCovMat(covMat.rows(), covMat.cols());
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gmm::dense_matrix<Scalar> eigvect(covMat.rows(), covMat.cols());
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std::vector<Scalar> eigval(covMat.rows());
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eiToGmm(covMat, gmmCovMat);
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for (int t=0; t<TRIES; ++t)
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{
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for (int t = 0; t < TRIES; ++t) {
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timerSa.start();
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for (int k=0; k<saRepeats; ++k)
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{
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for (int k = 0; k < saRepeats; ++k) {
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gmm::symmetric_qr_algorithm(gmmCovMat, eigval, eigvect);
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acc += eigvect(r,c);
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acc += eigvect(r, c);
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}
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timerSa.stop();
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}
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// the non-selfadjoint solver does not compute the eigen vectors
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// for (int t=0; t<TRIES; ++t)
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// {
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// timerStd.start();
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// for (int k=0; k<stdRepeats; ++k)
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// {
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// gmm::implicit_qr_algorithm(gmmCovMat, eigval, eigvect);
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// acc += eigvect(r,c);
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// }
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// timerStd.stop();
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// }
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// for (int t=0; t<TRIES; ++t)
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// {
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// timerStd.start();
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// for (int k=0; k<stdRepeats; ++k)
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// {
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// gmm::implicit_qr_algorithm(gmmCovMat, eigval, eigvect);
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// acc += eigvect(r,c);
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// }
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// timerStd.stop();
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// }
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std::cout << " | \t"
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<< timerSa.value() * REPEAT / saRepeats << "s"
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std::cout << " | \t" << timerSa.value() * REPEAT / saRepeats << "s"
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<< /*timerStd.value() * REPEAT / stdRepeats << "s"*/ " na ";
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}
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#endif
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#endif
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#ifdef BENCH_GSL
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if (MatrixType::RowsAtCompileTime==Dynamic)
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{
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#ifdef BENCH_GSL
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if (MatrixType::RowsAtCompileTime == Dynamic) {
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timerSa.reset();
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timerStd.reset();
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gsl_matrix* gslCovMat = gsl_matrix_alloc(covMat.rows(),covMat.cols());
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gsl_matrix* gslCopy = gsl_matrix_alloc(covMat.rows(),covMat.cols());
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gsl_matrix* eigvect = gsl_matrix_alloc(covMat.rows(),covMat.cols());
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gsl_vector* eigval = gsl_vector_alloc(covMat.rows());
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gsl_matrix* gslCovMat = gsl_matrix_alloc(covMat.rows(), covMat.cols());
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gsl_matrix* gslCopy = gsl_matrix_alloc(covMat.rows(), covMat.cols());
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gsl_matrix* eigvect = gsl_matrix_alloc(covMat.rows(), covMat.cols());
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gsl_vector* eigval = gsl_vector_alloc(covMat.rows());
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gsl_eigen_symmv_workspace* eisymm = gsl_eigen_symmv_alloc(covMat.rows());
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gsl_matrix_complex* eigvectz = gsl_matrix_complex_alloc(covMat.rows(),covMat.cols());
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gsl_vector_complex* eigvalz = gsl_vector_complex_alloc(covMat.rows());
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gsl_matrix_complex* eigvectz = gsl_matrix_complex_alloc(covMat.rows(), covMat.cols());
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gsl_vector_complex* eigvalz = gsl_vector_complex_alloc(covMat.rows());
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gsl_eigen_nonsymmv_workspace* einonsymm = gsl_eigen_nonsymmv_alloc(covMat.rows());
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eiToGsl(covMat, &gslCovMat);
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for (int t=0; t<TRIES; ++t)
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{
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for (int t = 0; t < TRIES; ++t) {
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timerSa.start();
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for (int k=0; k<saRepeats; ++k)
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{
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gsl_matrix_memcpy(gslCopy,gslCovMat);
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for (int k = 0; k < saRepeats; ++k) {
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gsl_matrix_memcpy(gslCopy, gslCovMat);
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gsl_eigen_symmv(gslCopy, eigval, eigvect, eisymm);
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acc += gsl_matrix_get(eigvect,r,c);
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acc += gsl_matrix_get(eigvect, r, c);
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}
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timerSa.stop();
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}
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for (int t=0; t<TRIES; ++t)
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{
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for (int t = 0; t < TRIES; ++t) {
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timerStd.start();
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for (int k=0; k<stdRepeats; ++k)
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{
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gsl_matrix_memcpy(gslCopy,gslCovMat);
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for (int k = 0; k < stdRepeats; ++k) {
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gsl_matrix_memcpy(gslCopy, gslCovMat);
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gsl_eigen_nonsymmv(gslCopy, eigvalz, eigvectz, einonsymm);
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acc += GSL_REAL(gsl_matrix_complex_get(eigvectz,r,c));
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acc += GSL_REAL(gsl_matrix_complex_get(eigvectz, r, c));
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}
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timerStd.stop();
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}
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std::cout << " | \t"
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<< timerSa.value() * REPEAT / saRepeats << "s \t"
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<< timerStd.value() * REPEAT / stdRepeats << "s";
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std::cout << " | \t" << timerSa.value() * REPEAT / saRepeats << "s \t" << timerStd.value() * REPEAT / stdRepeats
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<< "s";
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gsl_matrix_free(gslCovMat);
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gsl_vector_free(gslCopy);
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@@ -177,36 +161,32 @@ __attribute__ ((noinline)) void benchEigenSolver(const MatrixType& m)
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gsl_eigen_symmv_free(eisymm);
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gsl_eigen_nonsymmv_free(einonsymm);
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}
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#endif
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#endif
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std::cout << "\n";
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// make sure the compiler does not optimize too much
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if (acc==123)
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std::cout << acc;
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if (acc == 123) std::cout << acc;
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}
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int main(int argc, char* argv[])
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{
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const int dynsizes[] = {4,6,8,12,16,24,32,64,128,256,512,0};
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int main(int argc, char* argv[]) {
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const int dynsizes[] = {4, 6, 8, 12, 16, 24, 32, 64, 128, 256, 512, 0};
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std::cout << "size selfadjoint generic";
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#ifdef BENCH_GMM
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#ifdef BENCH_GMM
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std::cout << " GMM++ ";
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#endif
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#ifdef BENCH_GSL
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#endif
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#ifdef BENCH_GSL
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std::cout << " GSL (double + ATLAS) ";
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#endif
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#endif
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std::cout << "\n";
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for (uint i=0; dynsizes[i]>0; ++i)
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benchEigenSolver(Matrix<Scalar,Dynamic,Dynamic>(dynsizes[i],dynsizes[i]));
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for (uint i = 0; dynsizes[i] > 0; ++i) benchEigenSolver(Matrix<Scalar, Dynamic, Dynamic>(dynsizes[i], dynsizes[i]));
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benchEigenSolver(Matrix<Scalar,2,2>());
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benchEigenSolver(Matrix<Scalar,3,3>());
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benchEigenSolver(Matrix<Scalar,4,4>());
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benchEigenSolver(Matrix<Scalar,6,6>());
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benchEigenSolver(Matrix<Scalar,8,8>());
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benchEigenSolver(Matrix<Scalar,12,12>());
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benchEigenSolver(Matrix<Scalar,16,16>());
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benchEigenSolver(Matrix<Scalar, 2, 2>());
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benchEigenSolver(Matrix<Scalar, 3, 3>());
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benchEigenSolver(Matrix<Scalar, 4, 4>());
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benchEigenSolver(Matrix<Scalar, 6, 6>());
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benchEigenSolver(Matrix<Scalar, 8, 8>());
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benchEigenSolver(Matrix<Scalar, 12, 12>());
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benchEigenSolver(Matrix<Scalar, 16, 16>());
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return 0;
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}
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