Brief announcement: Hypergraph partitioning for parallel sparse matrix-matrix multiplication

Grey Ballard, Nicholas Knight, Alex Druinsky, Oded Schwartz

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

19 Scopus citations

Abstract

The performance of parallel algorithms for sparse matrixmatrix multiplication is typically determined by the amount of interprocessor communication performed, which in turn depends on the nonzero structure of the input matrices. In this paper, we characterize the communication cost of a sparse matrix-matrix multiplication algorithm in terms of the size of a cut of an associated hypergraph that encodes the computation for a given input nonzero structure. Obtaining an optimal algorithm corresponds to solving a hypergraph partitioning problem. Our hypergraph model generalizes several existing models for sparse matrix-vector multiplication, and we can leverage hypergraph partitioners developed for that computation to improve applicationspecific algorithms for multiplying sparse matrices.

Original languageAmerican English
Title of host publicationSPAA 2015 - Proceedings of the 27th ACM Symposium on Parallelism in Algorithms and Architectures
PublisherAssociation for Computing Machinery
Pages86-88
Number of pages3
ISBN (Electronic)9781450335881
DOIs
StatePublished - 13 Jun 2015
Event27th ACM Symposium on Parallelism in Algorithms and Architectures, SPAA 2015 - Portland, United States
Duration: 13 Jun 201515 Jun 2015

Publication series

NameAnnual ACM Symposium on Parallelism in Algorithms and Architectures
Volume2015-June

Conference

Conference27th ACM Symposium on Parallelism in Algorithms and Architectures, SPAA 2015
Country/TerritoryUnited States
CityPortland
Period13/06/1515/06/15

Bibliographical note

Publisher Copyright:
Copyright © 2015 ACM.

Fingerprint

Dive into the research topics of 'Brief announcement: Hypergraph partitioning for parallel sparse matrix-matrix multiplication'. Together they form a unique fingerprint.

Cite this