extend the sparse matrix assembly benchmark
diff --git a/bench/sparse_setter.cpp b/bench/sparse_setter.cpp
index 6f7a19d..9c22636 100644
--- a/bench/sparse_setter.cpp
+++ b/bench/sparse_setter.cpp
@@ -12,7 +12,15 @@
#endif
#ifndef REPEAT
-#define REPEAT 1
+#define REPEAT 2
+#endif
+
+#ifndef NBTRIES
+#define NBTRIES 2
+#endif
+
+#ifndef KK
+#define KK 10
#endif
#ifndef NOGOOGLE
@@ -22,7 +30,7 @@
#include "BenchSparseUtil.h"
-#define CHECK_MEM
+#define CHECK_MEM
// #define CHECK_MEM std/**/::cout << "check mem\n"; getchar();
#define BENCH(X) \
@@ -37,9 +45,13 @@
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);
@@ -50,17 +62,36 @@
{
int rows = SIZE;
int cols = SIZE;
- bool fullyrand = false;
- //float density = float(NBPERROW)/float(SIZE);
-
+ bool fullyrand = true;
+
BenchTimer timer;
Coordinates coords;
Values values;
if(fullyrand)
{
- for (int i=0; i<cols*NBPERROW; ++i)
+ Coordinates pool;
+ pool.reserve(cols*NBPERROW);
+ std::cerr << "fill pool" << "\n";
+ for (int i=0; i<cols*NBPERROW; )
{
- coords.push_back(Vector2i(ei_random<int>(0,rows-1),ei_random<int>(0,cols-1)));
+// DynamicSparseMatrix<int> stencil(SIZE,SIZE);
+ Vector2i ij(ei_random<int>(0,rows-1),ei_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 = ei_random<int>(0,pool.size());
+ coords.push_back(pool[i]);
values.push_back(ei_random<Scalar>());
}
}
@@ -79,67 +110,55 @@
// dense matrices
#ifdef DENSEMATRIX
{
- timer.reset();
- timer.start();
- for (int k=0; k<REPEAT; ++k)
- setrand_eigen_dense(coords,values);
- timer.stop();
+ BENCH(setrand_eigen_dense(coords,values);)
std::cout << "Eigen Dense\t" << timer.value() << "\n";
}
#endif
// eigen sparse matrices
- if (!fullyrand)
+// if (!fullyrand)
+// {
+// BENCH(setinnerrand_eigen(coords,values);)
+// std::cout << "Eigen fillrand\t" << timer.value() << "\n";
+// }
{
- timer.reset();
- timer.start();
- for (int k=0; k<REPEAT; ++k)
- setinnerrand_eigen(coords,values);
- timer.stop();
- 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";
}
{
- timer.reset();
- timer.start();
- for (int k=0; k<REPEAT; ++k)
- setrand_eigen_gnu_hash(coords,values);
- timer.stop();
- std::cout << "Eigen std::map\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
{
- timer.reset();
- timer.start();
- for (int k=0; k<REPEAT; ++k)
- setrand_eigen_google_dense(coords,values);
- timer.stop();
+ BENCH(setrand_eigen_google_dense(coords,values);)
std::cout << "Eigen google dense\t" << timer.value() << "\n";
}
{
- timer.reset();
- timer.start();
- for (int k=0; k<REPEAT; ++k)
- setrand_eigen_google_sparse(coords,values);
- timer.stop();
+ BENCH(setrand_eigen_google_sparse(coords,values);)
std::cout << "Eigen google sparse\t" << timer.value() << "\n";
}
#endif
-
+
#ifndef NOUBLAS
{
- timer.reset();
- timer.start();
- for (int k=0; k<REPEAT; ++k)
- setrand_ublas_mapped(coords,values);
- timer.stop();
- std::cout << "ublas mapped\t" << timer.value() << "\n";
+// BENCH(setrand_ublas_mapped(coords,values);)
+// std::cout << "ublas mapped\t" << timer.value() << "\n";
}
{
- timer.reset();
- timer.start();
- for (int k=0; k<REPEAT; ++k)
- setrand_ublas_genvec(coords,values);
- timer.stop();
+ BENCH(setrand_ublas_genvec(coords,values);)
std::cout << "ublas vecofvec\t" << timer.value() << "\n";
}
/*{
@@ -159,16 +178,12 @@
std::cout << "ublas coord\t" << timer.value() << "\n";
}*/
#endif
-
-
+
+
// MTL4
#ifndef NOMTL
{
- timer.reset();
- timer.start();
- for (int k=0; k<REPEAT; ++k)
- setrand_mtl(coords,values);
- timer.stop();
+ BENCH(setrand_mtl(coords,values));
std::cout << "MTL\t" << timer.value() << "\n";
}
#endif
@@ -180,16 +195,63 @@
{
using namespace Eigen;
SparseMatrix<Scalar> mat(SIZE,SIZE);
- mat.startFill(2000000/*coords.size()*/);
+ //mat.startFill(2000000/*coords.size()*/);
for (int i=0; i<coords.size(); ++i)
{
- mat.fillrand(coords[i].x(), coords[i].y()) = vals[i];
+ mat.insert(coords[i].x(), coords[i].y()) = vals[i];
}
- mat.endFill();
+ 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;
@@ -198,11 +260,11 @@
RandomSetter<SparseMatrix<Scalar>, StdMapTraits > setter(mat);
for (int i=0; i<coords.size(); ++i)
{
- setter(coords[i].x(), coords[i].y()) = vals[i];
+ setter(coords[i].x(), coords[i].y()) += vals[i];
}
CHECK_MEM;
}
- return 0;//&mat.coeffRef(coords[0].x(), coords[0].y());
+ return &mat.coeffRef(coords[0].x(), coords[0].y());
}
#ifndef NOGOOGLE
@@ -213,10 +275,10 @@
{
RandomSetter<SparseMatrix<Scalar>, GoogleDenseHashMapTraits> setter(mat);
for (int i=0; i<coords.size(); ++i)
- setter(coords[i].x(), coords[i].y()) = vals[i];
+ setter(coords[i].x(), coords[i].y()) += vals[i];
CHECK_MEM;
}
- return 0;//&mat.coeffRef(coords[0].x(), coords[0].y());
+ return &mat.coeffRef(coords[0].x(), coords[0].y());
}
EIGEN_DONT_INLINE Scalar* setrand_eigen_google_sparse(const Coordinates& coords, const Values& vals)
@@ -226,13 +288,139 @@
{
RandomSetter<SparseMatrix<Scalar>, GoogleSparseHashMapTraits> setter(mat);
for (int i=0; i<coords.size(); ++i)
- setter(coords[i].x(), coords[i].y()) = vals[i];
+ setter(coords[i].x(), coords[i].y()) += vals[i];
CHECK_MEM;
}
- return 0;//&mat.coeffRef(coords[0].x(), coords[0].y());
+ 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)
{
@@ -242,7 +430,7 @@
mapped_matrix<Scalar> aux(SIZE,SIZE);
for (int i=0; i<coords.size(); ++i)
{
- aux(coords[i].x(), coords[i].y()) = vals[i];
+ aux(coords[i].x(), coords[i].y()) += vals[i];
}
CHECK_MEM;
compressed_matrix<Scalar> mat(aux);
@@ -278,12 +466,12 @@
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];
+ aux(coords[i].x(), coords[i].y()) += vals[i];
}
CHECK_MEM;
compressed_matrix<Scalar,row_major> mat(aux);