FastDeploy/third_party/eigen/bench/sparse_dense_product.cpp

// g++ -O3 -g0 -DNDEBUG  sparse_product.cpp -I..
// -I/home/gael/Coding/LinearAlgebra/mtl4/ -DDENSITY=0.005 -DSIZE=10000 &&
// ./a.out
// g++ -O3 -g0 -DNDEBUG  sparse_product.cpp -I..
// -I/home/gael/Coding/LinearAlgebra/mtl4/ -DDENSITY=0.05 -DSIZE=2000 && ./a.out
// -DNOGMM -DNOMTL -DCSPARSE
// -I /home/gael/Coding/LinearAlgebra/CSparse/Include/
// /home/gael/Coding/LinearAlgebra/CSparse/Lib/libcsparse.a
#ifndef SIZE
#define SIZE 650000
#endif

#ifndef DENSITY
#define DENSITY 0.01
#endif

#ifndef REPEAT
#define REPEAT 1
#endif

#include "BenchSparseUtil.h"

#ifndef MINDENSITY
#define MINDENSITY 0.0004
#endif

#ifndef NBTRIES
#define NBTRIES 10
#endif

#define BENCH(X)                          \
  timer.reset();                          \
  for (int _j = 0; _j < NBTRIES; ++_j) {  \
    timer.start();                        \
    for (int _k = 0; _k < REPEAT; ++_k) { \
      X                                   \
    }                                     \
    timer.stop();                         \
  }

#ifdef CSPARSE
cs* cs_sorted_multiply(const cs* a, const cs* b) {
  cs* A = cs_transpose(a, 1);
  cs* B = cs_transpose(b, 1);
  cs* D = cs_multiply(B, A); /* D = B'*A' */
  cs_spfree(A);
  cs_spfree(B);
  cs_dropzeros(D);            /* drop zeros from D */
  cs* C = cs_transpose(D, 1); /* C = D', so that C is sorted */
  cs_spfree(D);
  return C;
}
#endif

int main(int argc, char* argv[]) {
  int rows = SIZE;
  int cols = SIZE;
  float density = DENSITY;

  EigenSparseMatrix sm1(rows, cols);
  DenseVector v1(cols), v2(cols);
  v1.setRandom();

  BenchTimer timer;
  for (float density = DENSITY; density >= MINDENSITY; density *= 0.5) {
    // fillMatrix(density, rows, cols, sm1);
    fillMatrix2(7, rows, cols, sm1);

// dense matrices
#ifdef DENSEMATRIX
    {
      std::cout << "Eigen Dense\t" << density * 100 << "%\n";
      DenseMatrix m1(rows, cols);
      eiToDense(sm1, m1);

      timer.reset();
      timer.start();
      for (int k = 0; k < REPEAT; ++k) v2 = m1 * v1;
      timer.stop();
      std::cout << "   a * v:\t" << timer.best() << "  "
                << double(REPEAT) / timer.best() << " * / sec " << endl;

      timer.reset();
      timer.start();
      for (int k = 0; k < REPEAT; ++k) v2 = m1.transpose() * v1;
      timer.stop();
      std::cout << "   a' * v:\t" << timer.best() << endl;
    }
#endif

    // eigen sparse matrices
    {
      std::cout << "Eigen sparse\t"
                << sm1.nonZeros() / float(sm1.rows() * sm1.cols()) * 100
                << "%\n";

      BENCH(asm("#myc"); v2 = sm1 * v1; asm("#myd");)
      std::cout << "   a * v:\t" << timer.best() / REPEAT << "  "
                << double(REPEAT) / timer.best(REAL_TIMER) << " * / sec "
                << endl;

      BENCH({
        asm("#mya");
        v2 = sm1.transpose() * v1;
        asm("#myb");
      })

      std::cout << "   a' * v:\t" << timer.best() / REPEAT << endl;
    }

//     {
//       DynamicSparseMatrix<Scalar> m1(sm1);
//       std::cout << "Eigen dyn-sparse\t" <<
//       m1.nonZeros()/float(m1.rows()*m1.cols())*100 << "%\n";
//
//       BENCH(for (int k=0; k<REPEAT; ++k) v2 = m1 * v1;)
//       std::cout << "   a * v:\t" << timer.value() << endl;
//
//       BENCH(for (int k=0; k<REPEAT; ++k) v2 = m1.transpose() * v1;)
//       std::cout << "   a' * v:\t" << timer.value() << endl;
//     }

// GMM++
#ifndef NOGMM
    {
      std::cout << "GMM++ sparse\t" << density * 100 << "%\n";
      // GmmDynSparse  gmmT3(rows,cols);
      GmmSparse m1(rows, cols);
      eiToGmm(sm1, m1);

      std::vector<Scalar> gmmV1(cols), gmmV2(cols);
      Map<Matrix<Scalar, Dynamic, 1> >(&gmmV1[0], cols) = v1;
      Map<Matrix<Scalar, Dynamic, 1> >(&gmmV2[0], cols) = v2;

      BENCH(asm("#myx"); gmm::mult(m1, gmmV1, gmmV2); asm("#myy");)
      std::cout << "   a * v:\t" << timer.value() << endl;

      BENCH(gmm::mult(gmm::transposed(m1), gmmV1, gmmV2);)
      std::cout << "   a' * v:\t" << timer.value() << endl;
    }
#endif

#ifndef NOUBLAS
    {
      std::cout << "ublas sparse\t" << density * 100 << "%\n";
      UBlasSparse m1(rows, cols);
      eiToUblas(sm1, m1);

      boost::numeric::ublas::vector<Scalar> uv1, uv2;
      eiToUblasVec(v1, uv1);
      eiToUblasVec(v2, uv2);

      //       std::vector<Scalar> gmmV1(cols), gmmV2(cols);
      //       Map<Matrix<Scalar,Dynamic,1> >(&gmmV1[0], cols) = v1;
      //       Map<Matrix<Scalar,Dynamic,1> >(&gmmV2[0], cols) = v2;

      BENCH(uv2 = boost::numeric::ublas::prod(m1, uv1);)
      std::cout << "   a * v:\t" << timer.value() << endl;

      //       BENCH( boost::ublas::prod(gmm::transposed(m1), gmmV1, gmmV2); )
      //       std::cout << "   a' * v:\t" << timer.value() << endl;
    }
#endif

// MTL4
#ifndef NOMTL
    {
      std::cout << "MTL4\t" << density * 100 << "%\n";
      MtlSparse m1(rows, cols);
      eiToMtl(sm1, m1);
      mtl::dense_vector<Scalar> mtlV1(cols, 1.0);
      mtl::dense_vector<Scalar> mtlV2(cols, 1.0);

      timer.reset();
      timer.start();
      for (int k = 0; k < REPEAT; ++k) mtlV2 = m1 * mtlV1;
      timer.stop();
      std::cout << "   a * v:\t" << timer.value() << endl;

      timer.reset();
      timer.start();
      for (int k = 0; k < REPEAT; ++k) mtlV2 = trans(m1) * mtlV1;
      timer.stop();
      std::cout << "   a' * v:\t" << timer.value() << endl;
    }
#endif

    std::cout << "\n\n";
  }

  return 0;
}