International audienceWe implement parallel and distributed versions of the sparse matrix-vector product and the sequence of matrixvector product operations, using OpenMP, MPI, and the ARM SVE intrinsic functions, for different matrix storage formats. We investigate the efficiency of these implementations on one and two A64FX processors, using a variety of sparse matrices as input. The matrices have different properties in size, sparsity and regularity. We observe that a parallel and distributed implementation shows good scaling on two nodes for cases where the matrix is close to a diagonal matrix, but the performances degrade quickly with variations to the sparsity or regularity of the input
Sparse Matrix-Vector Multiplication (SpMxV) is a widely used mathematical operation in many high-per...
The massive parallelism of graphics processing units (GPUs) offers tremendous performance in many hi...
Sparse matrix-vector multiplication is an integral part of many scientific algorithms. Several studi...
International audienceWe implement parallel and distributed versions of the sparse matrix-vector pro...
The matrix-vector product is one of the most important computational components of Krylov methods. T...
Sparse matrix-vector multiplications are essential in the numerical resolution of partial differenti...
The sparse matrix--vector multiplication is an important kernel, but is hard to efficiently execute ...
We contribute to the optimization of the sparse matrix-vector product by introducing a variant of th...
AbstractThe matrix-vector multiplication operation is the kernel of most numerical algorithms.Typica...
Sparse computations are ubiquitous in computational codes, with the sparse matrix-vector (SpMV) mult...
Sparse matrix-vector multiplication (SMVM) is a fundamental operation in many scientific and enginee...
Abstract. Many applications based on finite element and finite difference methods include the soluti...
In this paper we present a new technique for sparse matrix multiplication on vector multiprocessors ...
This paper presents a novel implementation of parallel sparse matrix-matrix multiplication using dis...
Due to copyright restrictions, the access to the full text of this article is only available via sub...
Sparse Matrix-Vector Multiplication (SpMxV) is a widely used mathematical operation in many high-per...
The massive parallelism of graphics processing units (GPUs) offers tremendous performance in many hi...
Sparse matrix-vector multiplication is an integral part of many scientific algorithms. Several studi...
International audienceWe implement parallel and distributed versions of the sparse matrix-vector pro...
The matrix-vector product is one of the most important computational components of Krylov methods. T...
Sparse matrix-vector multiplications are essential in the numerical resolution of partial differenti...
The sparse matrix--vector multiplication is an important kernel, but is hard to efficiently execute ...
We contribute to the optimization of the sparse matrix-vector product by introducing a variant of th...
AbstractThe matrix-vector multiplication operation is the kernel of most numerical algorithms.Typica...
Sparse computations are ubiquitous in computational codes, with the sparse matrix-vector (SpMV) mult...
Sparse matrix-vector multiplication (SMVM) is a fundamental operation in many scientific and enginee...
Abstract. Many applications based on finite element and finite difference methods include the soluti...
In this paper we present a new technique for sparse matrix multiplication on vector multiprocessors ...
This paper presents a novel implementation of parallel sparse matrix-matrix multiplication using dis...
Due to copyright restrictions, the access to the full text of this article is only available via sub...
Sparse Matrix-Vector Multiplication (SpMxV) is a widely used mathematical operation in many high-per...
The massive parallelism of graphics processing units (GPUs) offers tremendous performance in many hi...
Sparse matrix-vector multiplication is an integral part of many scientific algorithms. Several studi...