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Optimization in sparse-sparse matrix products for small ones

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This commit is contained in:
Gael Guennebaud 2014-08-29 14:19:03 +02:00
parent 460662cbcc
commit aec3d90ca6
1 changed files with 10 additions and 8 deletions
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16
Eigen/src/SparseCore/ConservativeSparseSparseProduct.h
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@ -25,9 +25,9 @@ static void conservative_sparse_sparse_product_impl(const Lhs& lhs, const Rhs& r
Index cols = rhs.outerSize(); Index cols = rhs.outerSize();
eigen_assert(lhs.outerSize() == rhs.innerSize()); eigen_assert(lhs.outerSize() == rhs.innerSize());
std::vector<bool> mask(rows,false); ei_declare_aligned_stack_constructed_variable(bool, mask, rows, 0);
Matrix<Scalar,Dynamic,1> values(rows); ei_declare_aligned_stack_constructed_variable(Scalar, values, rows, 0);
Matrix<Index,Dynamic,1> indices(rows); ei_declare_aligned_stack_constructed_variable(Index, indices, rows, 0);
// estimate the number of non zero entries // estimate the number of non zero entries
// given a rhs column containing Y non zeros, we assume that the respective Y columns // given a rhs column containing Y non zeros, we assume that the respective Y columns
@ -77,7 +77,7 @@ static void conservative_sparse_sparse_product_impl(const Lhs& lhs, const Rhs& r
else else
{ {
// alternative ordered insertion code: // alternative ordered insertion code:
const Index t200 = rows/(log2(200)*1.39); const Index t200 = rows/11; // 11 == (log2(200)*1.39)
const Index t = (rows*100)/139; const Index t = (rows*100)/139;
// FIXME reserve nnz non zeros // FIXME reserve nnz non zeros
@ -88,7 +88,7 @@ static void conservative_sparse_sparse_product_impl(const Lhs& lhs, const Rhs& r
// result is clearly very sparse we use a linear bound up to 200. // result is clearly very sparse we use a linear bound up to 200.
if((nnz<200 && nnz<t200) || nnz * log2(nnz) < t) if((nnz<200 && nnz<t200) || nnz * log2(nnz) < t)
{ {
if(nnz>1) std::sort(indices.data(),indices.data()+nnz); if(nnz>1) std::sort(indices,indices+nnz);
for(Index k=0; k<nnz; ++k) for(Index k=0; k<nnz; ++k)
{ {
Index i = indices[k]; Index i = indices[k];
@ -133,14 +133,16 @@ struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,C
static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res) static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
{ {
typedef SparseMatrix<typename ResultType::Scalar,RowMajor,typename ResultType::Index> RowMajorMatrix; typedef SparseMatrix<typename ResultType::Scalar,RowMajor,typename ResultType::Index> RowMajorMatrix;
typedef SparseMatrix<typename ResultType::Scalar,ColMajor,typename ResultType::Index> ColMajorMatrix; typedef SparseMatrix<typename ResultType::Scalar,ColMajor,typename ResultType::Index> ColMajorMatrixAux;
typedef typename sparse_eval<ColMajorMatrixAux,ResultType::RowsAtCompileTime,ResultType::ColsAtCompileTime>::type ColMajorMatrix;
ColMajorMatrix resCol(lhs.rows(),rhs.cols()); ColMajorMatrix resCol(lhs.rows(),rhs.cols());
// FIXME, the following heuristic is probably not very good. // FIXME, the following heuristic is probably not very good.
if(lhs.rows()>=rhs.cols()) if(lhs.rows()>=rhs.cols())
{ {
// perform sorted insertion // perform sorted insertion
internal::conservative_sparse_sparse_product_impl<Lhs,Rhs,ColMajorMatrix>(lhs, rhs, resCol, true); internal::conservative_sparse_sparse_product_impl<Lhs,Rhs,ColMajorMatrix>(lhs, rhs, resCol, true);
res = resCol; res.swap(resCol);
} }
else else
{ {