In our earlier work, we have investigated the feasibility of utilization of recursive partitioning in basic (BLAS oriented) sparse matrix computations, on multi-core cache-based computers. Following encouraging experimental results obtained for SpMV and SpSV operations, here we proceed to tune the storage format. To limit the memory bandwidth overhead we introduce usage of shorter (16 bit) indices in leaf sub matrices (at the end of the recursion). Experimental results obtained for the proposed approach on 8-core machines illustrate speed improvements, when performing sparse matrix-vector multiplication
Sparse computations are ubiquitous in computational codes, with the sparse matrix-vector (SpMV) mult...
Abstract. Sparse matrix-vector multiplication is an important computational kernel that tends to per...
Sparse matrix-vector multiplication (SpMV) is an important ker-nel in many scientific applications a...
Recently, we have proposed a recursive partitioning based layout for multi-core computations on spar...
Abstract—Recently, we have introduced an approach to basic sparse matrix computations on multicore c...
In earlier work we have introduced the “Recursive Sparse Blocks ” (RSB) sparse matrix storage scheme...
The thesis introduces a cache-oblivious method for the sparse matrix-vector (SpMV) multiplication, w...
Sparse matrix computations arise in many scientific computing problems and for some (e.g.: iterative...
In this article, we introduce a cache-oblivious method for sparse matrix–vector multiplication. Our ...
Due to copyright restrictions, the access to the full text of this article is only available via sub...
We improve the performance of sparse matrix-vector multiply (SpMV) on modern cache-based superscalar...
Sparse matrix-vector multiplication (shortly SpMV) is one of most common subroutines in the numerica...
Abstract—Sparse matrix-vector multiplication (SpM×V) has been characterized as one of the most signi...
An important kernel of scientific software is the multiplication of a sparse matrix by a vector. The...
It is well-known that reordering techniques applied to sparse matrices are common strategies to impr...
Sparse computations are ubiquitous in computational codes, with the sparse matrix-vector (SpMV) mult...
Abstract. Sparse matrix-vector multiplication is an important computational kernel that tends to per...
Sparse matrix-vector multiplication (SpMV) is an important ker-nel in many scientific applications a...
Recently, we have proposed a recursive partitioning based layout for multi-core computations on spar...
Abstract—Recently, we have introduced an approach to basic sparse matrix computations on multicore c...
In earlier work we have introduced the “Recursive Sparse Blocks ” (RSB) sparse matrix storage scheme...
The thesis introduces a cache-oblivious method for the sparse matrix-vector (SpMV) multiplication, w...
Sparse matrix computations arise in many scientific computing problems and for some (e.g.: iterative...
In this article, we introduce a cache-oblivious method for sparse matrix–vector multiplication. Our ...
Due to copyright restrictions, the access to the full text of this article is only available via sub...
We improve the performance of sparse matrix-vector multiply (SpMV) on modern cache-based superscalar...
Sparse matrix-vector multiplication (shortly SpMV) is one of most common subroutines in the numerica...
Abstract—Sparse matrix-vector multiplication (SpM×V) has been characterized as one of the most signi...
An important kernel of scientific software is the multiplication of a sparse matrix by a vector. The...
It is well-known that reordering techniques applied to sparse matrices are common strategies to impr...
Sparse computations are ubiquitous in computational codes, with the sparse matrix-vector (SpMV) mult...
Abstract. Sparse matrix-vector multiplication is an important computational kernel that tends to per...
Sparse matrix-vector multiplication (SpMV) is an important ker-nel in many scientific applications a...