Squashed 'third_party/eigen/' content from commit 61d72f6
Change-Id: Iccc90fa0b55ab44037f018046d2fcffd90d9d025
git-subtree-dir: third_party/eigen
git-subtree-split: 61d72f6383cfa842868c53e30e087b0258177257
diff --git a/bench/sparse_setter.cpp b/bench/sparse_setter.cpp
new file mode 100644
index 0000000..a9f0b11
--- /dev/null
+++ b/bench/sparse_setter.cpp
@@ -0,0 +1,485 @@
+
+//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
+