Hypergraph partitioning based models and methods for exploiting cache locality in sparse matrix-vector multiplication
type: Item
instance of: scholarly article
zbMATH Open document ID: 1278.65047
DOI: 10.1137/100813956
author: Kadir Akbudak
author: Enver Kayaaslan
author: Cevdet Aykanat
published in: SIAM Journal on Scientific Computing
publication date: 2013-09-11T00:00:00Z
full work available at URL: https://arxiv.org/abs/1202.3856
Mathematics Subject Classification ID: 65F30
Mathematics Subject Classification ID: 65F10
Mathematics Subject Classification ID: 65Y20
Mathematics Subject Classification ID: 65F50
zbMATH DE Number: 6207557
zbMATH Keywords: cache locality
zbMATH Keywords: sparse matrix
zbMATH Keywords: matrix-vector multiplication
zbMATH Keywords: matrix reordering
zbMATH Keywords: computational hypergraph model
zbMATH Keywords: hypergraph partitioning
zbMATH Keywords: traveling salesman problem
describes a project that uses: SparseMatrix
describes a project that uses: hMETIS
describes a project that uses: OSKI
describes a project that uses: PaToH
MaRDI profile type: Publication
OpenAlex ID: W3103552514
arXiv ID: 1202.3856
title: Hypergraph partitioning based models and methods for exploiting cache locality in sparse matrix-vector multiplication
review text: The authors investigate single and multiple sparse matrix-vector multiplications (SpMxV). For single SpMxV, they propose two cache-size-aware row/column reordering methods based on one-dimensional (1D) and two-dimensional (2D) top-down sparse matrix partitioning. They make use of the column-net hypergraph model for the 1D method and enhance the row-column-net hypergraph model for the 2D method. The authors evaluate performances of the proposed models and methods on a wide range of sparse matrices. They point out that the proposed methods outperform the available schemes, and that the methods based on 2D partitioning perform better than 1D partitioning methods.
reviewed by: R. P. Tewarson
description: scientific article; zbMATH DE number 6207557