bench/sparse_setter.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 100000
#endif

#ifndef NBPERROW
#define NBPERROW 24
#endif

#ifndef REPEAT
#define REPEAT 2
#endif

#ifndef NBTRIES
#define NBTRIES 2
#endif

#ifndef KK
#define KK 10
#endif

#ifndef NOGOOGLE
#define EIGEN_GOOGLEHASH_SUPPORT
#include <google/sparse_hash_map>
#endif

#include "BenchSparseUtil.h"

#define CHECK_MEM
// #define CHECK_MEM  std/**/::cout << "check mem\n"; getchar();

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

typedef std::vector<Vector2i> Coordinates;
typedef std::vector<float> Values;

EIGEN_DONT_INLINE Scalar* setinnerrand_eigen(const Coordinates& coords, const Values& vals);
EIGEN_DONT_INLINE Scalar* setrand_eigen_dynamic(const Coordinates& coords, const Values& vals);
EIGEN_DONT_INLINE Scalar* setrand_eigen_compact(const Coordinates& coords, const Values& vals);
EIGEN_DONT_INLINE Scalar* setrand_eigen_sumeq(const Coordinates& coords, const Values& vals);
EIGEN_DONT_INLINE Scalar* setrand_eigen_gnu_hash(const Coordinates& coords, const Values& vals);
EIGEN_DONT_INLINE Scalar* setrand_eigen_google_dense(const Coordinates& coords, const Values& vals);
EIGEN_DONT_INLINE Scalar* setrand_eigen_google_sparse(const Coordinates& coords, const Values& vals);
EIGEN_DONT_INLINE Scalar* setrand_scipy(const Coordinates& coords, const Values& vals);
EIGEN_DONT_INLINE Scalar* setrand_ublas_mapped(const Coordinates& coords, const Values& vals);
EIGEN_DONT_INLINE Scalar* setrand_ublas_coord(const Coordinates& coords, const Values& vals);
EIGEN_DONT_INLINE Scalar* setrand_ublas_compressed(const Coordinates& coords, const Values& vals);
EIGEN_DONT_INLINE Scalar* setrand_ublas_genvec(const Coordinates& coords, const Values& vals);
EIGEN_DONT_INLINE Scalar* setrand_mtl(const Coordinates& coords, const Values& vals);

int main(int argc, char* argv[]) {
  int rows = SIZE;
  int cols = SIZE;
  bool fullyrand = true;

  BenchTimer timer;
  Coordinates coords;
  Values values;
  if (fullyrand) {
    Coordinates pool;
    pool.reserve(cols * NBPERROW);
    std::cerr << "fill pool"
              << "\n";
    for (int i = 0; i < cols * NBPERROW;) {
      //       DynamicSparseMatrix<int> stencil(SIZE,SIZE);
      Vector2i ij(internal::random<int>(0, rows - 1), internal::random<int>(0, cols - 1));
      //       if(stencil.coeffRef(ij.x(), ij.y())==0)
      {
        //         stencil.coeffRef(ij.x(), ij.y()) = 1;
        pool.push_back(ij);
      }
      ++i;
    }
    std::cerr << "pool ok"
              << "\n";
    int n = cols * NBPERROW * KK;
    coords.reserve(n);
    values.reserve(n);
    for (int i = 0; i < n; ++i) {
      int i = internal::random<int>(0, pool.size());
      coords.push_back(pool[i]);
      values.push_back(internal::random<Scalar>());
    }
  } else {
    for (int j = 0; j < cols; ++j)
      for (int i = 0; i < NBPERROW; ++i) {
        coords.push_back(Vector2i(internal::random<int>(0, rows - 1), j));
        values.push_back(internal::random<Scalar>());
      }
  }
  std::cout << "nnz = " << coords.size() << "\n";
  CHECK_MEM
// dense matrices
#ifdef DENSEMATRIX
  {
    BENCH(setrand_eigen_dense(coords, values);)
    std::cout << "Eigen Dense\t" << timer.value() << "\n";
  }
#endif

  // eigen sparse matrices
  //     if (!fullyrand)
  //     {
  //       BENCH(setinnerrand_eigen(coords,values);)
  //       std::cout << "Eigen fillrand\t" << timer.value() << "\n";
  //     }
  {
    BENCH(setrand_eigen_dynamic(coords, values);)
    std::cout << "Eigen dynamic\t" << timer.value() << "\n";
  }
  //     {
  //       BENCH(setrand_eigen_compact(coords,values);)
  //       std::cout << "Eigen compact\t" << timer.value() << "\n";
  //     }
  {
    BENCH(setrand_eigen_sumeq(coords, values);)
    std::cout << "Eigen sumeq\t" << timer.value() << "\n";
  }
  {
    //       BENCH(setrand_eigen_gnu_hash(coords,values);)
    //       std::cout << "Eigen std::map\t" << timer.value() << "\n";
  }
  {
    BENCH(setrand_scipy(coords, values);)
    std::cout << "scipy\t" << timer.value() << "\n";
  }
#ifndef NOGOOGLE
  {
    BENCH(setrand_eigen_google_dense(coords, values);)
    std::cout << "Eigen google dense\t" << timer.value() << "\n";
  }
  {
    BENCH(setrand_eigen_google_sparse(coords, values);)
    std::cout << "Eigen google sparse\t" << timer.value() << "\n";
  }
#endif

#ifndef NOUBLAS
  {
      //       BENCH(setrand_ublas_mapped(coords,values);)
      //       std::cout << "ublas mapped\t" << timer.value() << "\n";
  } {
    BENCH(setrand_ublas_genvec(coords, values);)
    std::cout << "ublas vecofvec\t" << timer.value() << "\n";
  }
/*{
  timer.reset();
  timer.start();
  for (int k=0; k<REPEAT; ++k)
    setrand_ublas_compressed(coords,values);
  timer.stop();
  std::cout << "ublas comp\t" << timer.value() << "\n";
}
{
  timer.reset();
  timer.start();
  for (int k=0; k<REPEAT; ++k)
    setrand_ublas_coord(coords,values);
  timer.stop();
  std::cout << "ublas coord\t" << timer.value() << "\n";
}*/
#endif

// MTL4
#ifndef NOMTL
  {
    BENCH(setrand_mtl(coords, values));
    std::cout << "MTL\t" << timer.value() << "\n";
  }
#endif

  return 0;
}

EIGEN_DONT_INLINE Scalar* setinnerrand_eigen(const Coordinates& coords, const Values& vals) {
  using namespace Eigen;
  SparseMatrix<Scalar> mat(SIZE, SIZE);
  // mat.startFill(2000000/*coords.size()*/);
  for (int i = 0; i < coords.size(); ++i) {
    mat.insert(coords[i].x(), coords[i].y()) = vals[i];
  }
  mat.finalize();
  CHECK_MEM;
  return 0;
}

EIGEN_DONT_INLINE Scalar* setrand_eigen_dynamic(const Coordinates& coords, const Values& vals) {
  using namespace Eigen;
  DynamicSparseMatrix<Scalar> mat(SIZE, SIZE);
  mat.reserve(coords.size() / 10);
  for (int i = 0; i < coords.size(); ++i) {
    mat.coeffRef(coords[i].x(), coords[i].y()) += vals[i];
  }
  mat.finalize();
  CHECK_MEM;
  return &mat.coeffRef(coords[0].x(), coords[0].y());
}

EIGEN_DONT_INLINE Scalar* setrand_eigen_sumeq(const Coordinates& coords, const Values& vals) {
  using namespace Eigen;
  int n = coords.size() / KK;
  DynamicSparseMatrix<Scalar> mat(SIZE, SIZE);
  for (int j = 0; j < KK; ++j) {
    DynamicSparseMatrix<Scalar> aux(SIZE, SIZE);
    mat.reserve(n);
    for (int i = j * n; i < (j + 1) * n; ++i) {
      aux.insert(coords[i].x(), coords[i].y()) += vals[i];
    }
    aux.finalize();
    mat += aux;
  }
  return &mat.coeffRef(coords[0].x(), coords[0].y());
}

EIGEN_DONT_INLINE Scalar* setrand_eigen_compact(const Coordinates& coords, const Values& vals) {
  using namespace Eigen;
  DynamicSparseMatrix<Scalar> setter(SIZE, SIZE);
  setter.reserve(coords.size() / 10);
  for (int i = 0; i < coords.size(); ++i) {
    setter.coeffRef(coords[i].x(), coords[i].y()) += vals[i];
  }
  SparseMatrix<Scalar> mat = setter;
  CHECK_MEM;
  return &mat.coeffRef(coords[0].x(), coords[0].y());
}

EIGEN_DONT_INLINE Scalar* setrand_eigen_gnu_hash(const Coordinates& coords, const Values& vals) {
  using namespace Eigen;
  SparseMatrix<Scalar> mat(SIZE, SIZE);
  {
    RandomSetter<SparseMatrix<Scalar>, StdMapTraits> setter(mat);
    for (int i = 0; i < coords.size(); ++i) {
      setter(coords[i].x(), coords[i].y()) += vals[i];
    }
    CHECK_MEM;
  }
  return &mat.coeffRef(coords[0].x(), coords[0].y());
}

#ifndef NOGOOGLE
EIGEN_DONT_INLINE Scalar* setrand_eigen_google_dense(const Coordinates& coords, const Values& vals) {
  using namespace Eigen;
  SparseMatrix<Scalar> mat(SIZE, SIZE);
  {
    RandomSetter<SparseMatrix<Scalar>, GoogleDenseHashMapTraits> setter(mat);
    for (int i = 0; i < coords.size(); ++i) setter(coords[i].x(), coords[i].y()) += vals[i];
    CHECK_MEM;
  }
  return &mat.coeffRef(coords[0].x(), coords[0].y());
}

EIGEN_DONT_INLINE Scalar* setrand_eigen_google_sparse(const Coordinates& coords, const Values& vals) {
  using namespace Eigen;
  SparseMatrix<Scalar> mat(SIZE, SIZE);
  {
    RandomSetter<SparseMatrix<Scalar>, GoogleSparseHashMapTraits> setter(mat);
    for (int i = 0; i < coords.size(); ++i) setter(coords[i].x(), coords[i].y()) += vals[i];
    CHECK_MEM;
  }
  return &mat.coeffRef(coords[0].x(), coords[0].y());
}
#endif

template <class T>
void coo_tocsr(const int n_row, const int n_col, const int nnz, const Coordinates Aij, const Values Ax, int Bp[],
               int Bj[], T Bx[]) {
  // compute number of non-zero entries per row of A coo_tocsr
  std::fill(Bp, Bp + n_row, 0);

  for (int n = 0; n < nnz; n++) {
    Bp[Aij[n].x()]++;
  }

  // cumsum the nnz per row to get Bp[]
  for (int i = 0, cumsum = 0; i < n_row; i++) {
    int temp = Bp[i];
    Bp[i] = cumsum;
    cumsum += temp;
  }
  Bp[n_row] = nnz;

  // write Aj,Ax into Bj,Bx
  for (int n = 0; n < nnz; n++) {
    int row = Aij[n].x();
    int dest = Bp[row];

    Bj[dest] = Aij[n].y();
    Bx[dest] = Ax[n];

    Bp[row]++;
  }

  for (int i = 0, last = 0; i <= n_row; i++) {
    int temp = Bp[i];
    Bp[i] = last;
    last = temp;
  }

  // now Bp,Bj,Bx form a CSR representation (with possible duplicates)
}

template <class T1, class T2>
bool kv_pair_less(const std::pair<T1, T2>& x, const std::pair<T1, T2>& y) {
  return x.first < y.first;
}

template <class I, class T>
void csr_sort_indices(const I n_row, const I Ap[], I Aj[], T Ax[]) {
  std::vector<std::pair<I, T> > temp;

  for (I i = 0; i < n_row; i++) {
    I row_start = Ap[i];
    I row_end = Ap[i + 1];

    temp.clear();

    for (I jj = row_start; jj < row_end; jj++) {
      temp.push_back(std::make_pair(Aj[jj], Ax[jj]));
    }

    std::sort(temp.begin(), temp.end(), kv_pair_less<I, T>);

    for (I jj = row_start, n = 0; jj < row_end; jj++, n++) {
      Aj[jj] = temp[n].first;
      Ax[jj] = temp[n].second;
    }
  }
}

template <class I, class T>
void csr_sum_duplicates(const I n_row, const I n_col, I Ap[], I Aj[], T Ax[]) {
  I nnz = 0;
  I row_end = 0;
  for (I i = 0; i < n_row; i++) {
    I jj = row_end;
    row_end = Ap[i + 1];
    while (jj < row_end) {
      I j = Aj[jj];
      T x = Ax[jj];
      jj++;
      while (jj < row_end && Aj[jj] == j) {
        x += Ax[jj];
        jj++;
      }
      Aj[nnz] = j;
      Ax[nnz] = x;
      nnz++;
    }
    Ap[i + 1] = nnz;
  }
}

EIGEN_DONT_INLINE Scalar* setrand_scipy(const Coordinates& coords, const Values& vals) {
  using namespace Eigen;
  SparseMatrix<Scalar> mat(SIZE, SIZE);
  mat.resizeNonZeros(coords.size());
  //   std::cerr << "setrand_scipy...\n";
  coo_tocsr<Scalar>(SIZE, SIZE, coords.size(), coords, vals, mat._outerIndexPtr(), mat._innerIndexPtr(),
                    mat._valuePtr());
  //   std::cerr << "coo_tocsr ok\n";

  csr_sort_indices(SIZE, mat._outerIndexPtr(), mat._innerIndexPtr(), mat._valuePtr());

  csr_sum_duplicates(SIZE, SIZE, mat._outerIndexPtr(), mat._innerIndexPtr(), mat._valuePtr());

  mat.resizeNonZeros(mat._outerIndexPtr()[SIZE]);

  return &mat.coeffRef(coords[0].x(), coords[0].y());
}

#ifndef NOUBLAS
EIGEN_DONT_INLINE Scalar* setrand_ublas_mapped(const Coordinates& coords, const Values& vals) {
  using namespace boost;
  using namespace boost::numeric;
  using namespace boost::numeric::ublas;
  mapped_matrix<Scalar> aux(SIZE, SIZE);
  for (int i = 0; i < coords.size(); ++i) {
    aux(coords[i].x(), coords[i].y()) += vals[i];
  }
  CHECK_MEM;
  compressed_matrix<Scalar> mat(aux);
  return 0;  // &mat(coords[0].x(), coords[0].y());
}
/*EIGEN_DONT_INLINE Scalar* setrand_ublas_coord(const Coordinates& coords, const Values& vals)
{
  using namespace boost;
  using namespace boost::numeric;
  using namespace boost::numeric::ublas;
  coordinate_matrix<Scalar> aux(SIZE,SIZE);
  for (int i=0; i<coords.size(); ++i)
  {
    aux(coords[i].x(), coords[i].y()) = vals[i];
  }
  compressed_matrix<Scalar> mat(aux);
  return 0;//&mat(coords[0].x(), coords[0].y());
}
EIGEN_DONT_INLINE Scalar* setrand_ublas_compressed(const Coordinates& coords, const Values& vals)
{
  using namespace boost;
  using namespace boost::numeric;
  using namespace boost::numeric::ublas;
  compressed_matrix<Scalar> mat(SIZE,SIZE);
  for (int i=0; i<coords.size(); ++i)
  {
    mat(coords[i].x(), coords[i].y()) = vals[i];
  }
  return 0;//&mat(coords[0].x(), coords[0].y());
}*/
EIGEN_DONT_INLINE Scalar* setrand_ublas_genvec(const Coordinates& coords, const Values& vals) {
  using namespace boost;
  using namespace boost::numeric;
  using namespace boost::numeric::ublas;

  //   ublas::vector<coordinate_vector<Scalar> > foo;
  generalized_vector_of_vector<Scalar, row_major, ublas::vector<coordinate_vector<Scalar> > > aux(SIZE, SIZE);
  for (int i = 0; i < coords.size(); ++i) {
    aux(coords[i].x(), coords[i].y()) += vals[i];
  }
  CHECK_MEM;
  compressed_matrix<Scalar, row_major> mat(aux);
  return 0;  //&mat(coords[0].x(), coords[0].y());
}
#endif

#ifndef NOMTL
EIGEN_DONT_INLINE void setrand_mtl(const Coordinates& coords, const Values& vals);
#endif