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eigen/bench/sparse_setter.cpp

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//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
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