A parallel computing method using blocked format with optimal partitioning for SpMV on GPU
type: Item
instance of: scholarly article
title: A parallel computing method using blocked format with optimal partitioning for SpMV on GPU
zbMATH Open document ID: 1383.65041
author: Wangdong Yang
author: Keqin Li
author: KenLi Li
published in: Journal of Computer and System Sciences
publication date: 2017-11-14T00:00:00Z
Mathematics Subject Classification ID: 65F50
Mathematics Subject Classification ID: 65Y05
zbMATH DE Number: 6806909
zbMATH Keywords: blocked format
zbMATH Keywords: CPU/GPU
zbMATH Keywords: dynamic programming
zbMATH Keywords: heterogeneous parallel computing
zbMATH Keywords: partitioning
zbMATH Keywords: reordering
zbMATH Keywords: sparse matrix-vector multiplication
zbMATH Keywords: large-scale sparse matrices
describes a project that uses: MKL
describes a project that uses: CUSPARSE
describes a project that uses: CUDA
describes a project that uses: METIS
describes a project that uses: SparseMatrix
describes a project that uses: Sparsity
describes a project that uses: CUBLAS
MaRDI profile type: Publication
full work available at URL: j.jcss.2017.09.010
OpenAlex ID: W2766501636
Wikidata QID: Q126082539
cites work: The university of Florida sparse matrix collection
cites work: Towards dense linear algebra for hybrid GPU accelerated manycore systems
cites work: Sparse matrix-vector multiplication on the single-chip cloud computer many-core processor
cites work: Improved Three-Way Split Formulas for Binary Polynomial and Toeplitz Matrix Vector Products
cites work: Multiway Splitting Method for Toeplitz Matrix Vector Product
cites work: An Approximate Minimum Degree Ordering Algorithm
cites work: Nested Dissection of a Regular Finite Element Mesh
cites work: Performance Optimization Using Partitioned SpMV on GPUs and Multicore CPUs
DOI: 10.1016/J.JCSS.2017.09.010
description: scientific article; zbMATH DE number 6806909