add Cholesky and eigensolver benchmark

1 files changed, 210 insertions, 0 deletions

diff --git a/bench/benchEigenSolver.cpp b/bench/benchEigenSolver.cpp
new file mode 100644
index 000000000..a62ee41f4
--- /dev/null
+++ b/bench/benchEigenSolver.cpp
@@ -0,0 +1,210 @@
+
+// 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 <Eigen/Array>
+#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 = ei_random<int>(0,covMat.rows()-1);
+  int c = ei_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;
+}
+