Skip navigation
Skip navigation

Runtime sparse matrix format selection

Armstrong, Warren; Rendell, Alistair

Description

There exist many storage formats for the in-memory representation of sparse matrices. Choosing the format that yields the quickest processing of any given sparse matrix requires considering the exact non-zero structure of the matrix, as well as the current execution environment. Each of these factors can change at runtime. The matrix structure can vary as computation progresses, while the environment can change due to varying system load, the live migration of jobs across a heterogeneous...[Show more]

dc.contributor.authorArmstrong, Warren
dc.contributor.authorRendell, Alistair
dc.date.accessioned2010-10-06T02:48:46Z
dc.date.accessioned2010-12-20T06:04:16Z
dc.date.available2010-10-06T02:48:46Z
dc.date.available2010-12-20T06:04:16Z
dc.identifier.citationProcedia Computer Science 1.1 (2010): 135-144
dc.identifier.issn1877-0509
dc.identifier.urihttp://hdl.handle.net/10440/1116
dc.identifier.urihttp://digitalcollections.anu.edu.au/handle/10440/1116
dc.description.abstractThere exist many storage formats for the in-memory representation of sparse matrices. Choosing the format that yields the quickest processing of any given sparse matrix requires considering the exact non-zero structure of the matrix, as well as the current execution environment. Each of these factors can change at runtime. The matrix structure can vary as computation progresses, while the environment can change due to varying system load, the live migration of jobs across a heterogeneous cluster, etc. This paper describes an algorithm that learns at runtime how to map sparse matrices onto the format which provides the quickest sparse matrix-vector product calculation, and which can adapt to the hardware platform changing underfoot. We show multiplication times reduced by over 10% compared with the best non-adaptive format selection.
dc.format10 pages
dc.publisherElsevier
dc.rightsPermission to submit this article has been granted by the author. 23/09/10
dc.sourceProcedia Computer Science
dc.source.urihttp://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B9865-506HM1Y-H&_user=554534&_coverDate=05%2F31%2F2010&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_acct=C000028338&_version=1&_urlVersion=0&_userid=554534&md5=923dde090734af5adc3e932a21fe92d5&searchtype=a
dc.subjectsparse matrix formats
dc.subjectruntime tuning
dc.titleRuntime sparse matrix format selection
dc.typeJournal article
local.description.notesPaper presented at the "International Conference on Computational Science, ICCS 2010"
local.identifier.citationvolume1
dc.date.issued2010-05
local.identifier.absfor080399 - Computer Software not elsewhere classified
local.identifier.ariespublicationU3594520xPUB327
local.publisher.urlhttp://www.elsevier.com/wps/find/homepage.cws_home
local.type.statusAccepted Version
local.contributor.affiliationArmstrong, Warren, College of Engineering and Computer Science, ANU
local.contributor.affiliationRendell, Alistair, College of Engineering and Computer Science, ANU
local.bibliographicCitation.issue2010
local.bibliographicCitation.startpage1
local.bibliographicCitation.lastpage10
local.identifier.doi10.1016/j.procs.2010.04.016
local.identifier.absseo970108 - Expanding Knowledge in the Information and Computing Sciences
dc.date.updated2015-12-09T10:07:50Z
local.identifier.scopusID2-s2.0-78650258166
local.identifier.thomsonID000281951600015
CollectionsANU Research Publications

Download

File Description SizeFormat Image
Armstrong_Runtime2010.pdf133.91 kBAdobe PDFThumbnail


Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.

Updated:  19 May 2020/ Responsible Officer:  University Librarian/ Page Contact:  Library Systems & Web Coordinator