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							212 lines
						
					
					
						
							5.7 KiB
						
					
					
				| 
 | |
| // 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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