International audienceWe propose different implementations of the sparse matrix--dense vector multiplication (\spmv{}) for finite fields and rings $\Zb/m\Zb$. We take advantage of graphic card processors (GPU) and multi-core architectures. Our aim is to improve the speed of \spmv{} in the \linbox library, and henceforth the speed of its black box algorithms. Besides, we use this and a new parallelization of the sigma-basis algorithm in a parallel block Wiedemann rank implementation over finite fields
Sparse matrix-vector multiplication is an integral part of many scientific algorithms. Several studi...
Sparse matrix-vector multiplication (SpMV) is an important ker-nel in many scientific applications a...
Sparse matrix–vector multiplication (SpMV) is of singular importance in sparse linear algebra, which...
We propose different implementations of the sparse matrix–dense vec-tor multiplication (SpMV) for fi...
We implement a promising algorithm for sparse-matrix sparse-vector multiplication (SpMSpV) on the GP...
The massive parallelism of graphics processing units (GPUs) offers tremendous performance in many hi...
AbstractThe sparse matrix-vector multiplication (SpMV) is a fundamental kernel used in computational...
A wide class of finite-element (FE) electromagnetic applications requires computing very large spars...
This repository contains the code and scripts for verifying the claims in the paper "Design Principl...
We implement two novel algorithms for sparse-matrix dense-matrix multiplication (SpMM) on the GPU. O...
The multiplication of a sparse matrix by a dense vector (SpMV) is a centerpiece of scientific comput...
The sparse matrix-vector (SpMV) multiplication routine is an important building block used in many i...
AbstractSparse matrix vector multiplication (SpMV) is the dominant kernel in scientific simulations....
Sparse matrix-vector multiplication (spMVM) is the dominant operation in many sparse solvers. We in...
Sparse computations are ubiquitous in computational codes, with the sparse matrix-vector (SpMV) mult...
Sparse matrix-vector multiplication is an integral part of many scientific algorithms. Several studi...
Sparse matrix-vector multiplication (SpMV) is an important ker-nel in many scientific applications a...
Sparse matrix–vector multiplication (SpMV) is of singular importance in sparse linear algebra, which...
We propose different implementations of the sparse matrix–dense vec-tor multiplication (SpMV) for fi...
We implement a promising algorithm for sparse-matrix sparse-vector multiplication (SpMSpV) on the GP...
The massive parallelism of graphics processing units (GPUs) offers tremendous performance in many hi...
AbstractThe sparse matrix-vector multiplication (SpMV) is a fundamental kernel used in computational...
A wide class of finite-element (FE) electromagnetic applications requires computing very large spars...
This repository contains the code and scripts for verifying the claims in the paper "Design Principl...
We implement two novel algorithms for sparse-matrix dense-matrix multiplication (SpMM) on the GPU. O...
The multiplication of a sparse matrix by a dense vector (SpMV) is a centerpiece of scientific comput...
The sparse matrix-vector (SpMV) multiplication routine is an important building block used in many i...
AbstractSparse matrix vector multiplication (SpMV) is the dominant kernel in scientific simulations....
Sparse matrix-vector multiplication (spMVM) is the dominant operation in many sparse solvers. We in...
Sparse computations are ubiquitous in computational codes, with the sparse matrix-vector (SpMV) mult...
Sparse matrix-vector multiplication is an integral part of many scientific algorithms. Several studi...
Sparse matrix-vector multiplication (SpMV) is an important ker-nel in many scientific applications a...
Sparse matrix–vector multiplication (SpMV) is of singular importance in sparse linear algebra, which...