A Sparse Linear System Solver Used in a Distributed and Heterogenous Grid Computing Environment
Identifieur interne : 002158 ( Istex/Corpus ); précédent : 002157; suivant : 002159A Sparse Linear System Solver Used in a Distributed and Heterogenous Grid Computing Environment
Auteurs : Christophe Denis ; Raphael Couturier ; Fabienne JézéquelSource :
- Springer Optimization and Its Applications [ 1931-6828 ]
Abstract
Abstract: Many scientific applications need to solve very large sparse linear systems in order to simulate phenomena close to the reality. Grid computing is an answer to the growing demand of computational power. In a grid computing environment, communication times are significant and the bandwidth is variable, therefore frequent synchronizations slow down performances. Thus it is desirable to reduce the number of synchronizations in a parallel direct algorithm. Inspired from multisplitting techniques, the GREMLINS (GRid Efficient Methods for LINear Systems) solver we developed consists of solving several linear problems obtained by splitting. The principle of the balancing algorithm is presented, and experimental results are given.
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DOI: 10.1007/978-0-387-09707-7_4
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Grid computing is an answer to the growing demand of computational power. In a grid computing environment, communication times are significant and the bandwidth is variable, therefore frequent synchronizations slow down performances. Thus it is desirable to reduce the number of synchronizations in a parallel direct algorithm. Inspired from multisplitting techniques, the GREMLINS (GRid Efficient Methods for LINear Systems) solver we developed consists of solving several linear problems obtained by splitting. 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type="first">Ding-Zhu</forename><surname>Du</surname></persName><affiliation><orgName type="institution">University of Texas</orgName><address><settlement>Dallas</settlement></address></affiliation></editor><idno type="pISSN">1931-6828</idno><idno type="seriesID">7393</idno></series></biblStruct></sourceDesc></fileDesc><profileDesc><abstract xml:lang="en"><head>Abstract</head><p>Many scientific applications need to solve very large sparse linear systems in order to simulate phenomena close to the reality. Grid computing is an answer to the growing demand of computational power. In a grid computing environment, communication times are significant and the bandwidth is variable, therefore frequent synchronizations slow down performances. Thus it is desirable to reduce the number of synchronizations in a parallel direct algorithm. Inspired from multisplitting techniques, the GREMLINS (GRid Efficient Methods for LINear Systems) solver we developed consists of solving several linear problems obtained by splitting. 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Environment</ChapterTitle><ChapterFirstPage>47</ChapterFirstPage><ChapterLastPage>56</ChapterLastPage><ChapterCopyright><CopyrightHolderName>Springer Science+Business Media, LLC</CopyrightHolderName><CopyrightYear>2009</CopyrightYear></ChapterCopyright><ChapterGrants Type="Regular"><MetadataGrant Grant="OpenAccess"></MetadataGrant><AbstractGrant Grant="OpenAccess"></AbstractGrant><BodyPDFGrant Grant="Restricted"></BodyPDFGrant><BodyHTMLGrant Grant="Restricted"></BodyHTMLGrant><BibliographyGrant Grant="Restricted"></BibliographyGrant><ESMGrant Grant="Restricted"></ESMGrant></ChapterGrants><ChapterContext><SeriesID>7393</SeriesID><PartID>1</PartID><BookID>978-0-387-09707-7</BookID><BookTitle>Parallel Scientific Computing and Optimization</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff7"><AuthorName DisplayOrder="Western"><GivenName>Christophe</GivenName><FamilyName>Denis</FamilyName></AuthorName><Contact><Email>C.Denis@qub.ac.uk</Email></Contact></Author><Author AffiliationIDS="Aff8"><AuthorName DisplayOrder="Western"><GivenName>Raphael</GivenName><FamilyName>Couturier</FamilyName></AuthorName><Contact><Email>Raphael.Couturier@iut-bm.univ-fcomte.fr</Email></Contact></Author><Author AffiliationIDS="Aff9"><AuthorName DisplayOrder="Western"><GivenName>Fabienne</GivenName><FamilyName>Jézéquel</FamilyName></AuthorName><Contact><Email>Christophe.Denis@lip6.fr</Email></Contact></Author><Affiliation ID="Aff7"><OrgDivision>School of Electronics Electrical Engineering & Computer Science</OrgDivision><OrgName>The Queen’s University of Belfast</OrgName><OrgAddress><City>Belfast BT7 1NN</City><Country>UK</Country></OrgAddress></Affiliation><Affiliation ID="Aff8"><OrgName>Laboratoire d Informatique del Université de Franche-Comté</OrgName><OrgAddress><City>BP 527</City><State>90016 Belfort Cedex</State><Country>France</Country></OrgAddress></Affiliation><Affiliation ID="Aff9"><OrgDivision>Laboratoire d’Informatique LIP6</OrgDivision><OrgName>UPMC Univ Paris 06</OrgName><OrgAddress><Street>4 place Jussieu</Street><State>75252 Paris Cedex 05</State><Country>France</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para TextBreak="No">Many scientific applications need to solve very large sparse linear systems in order to simulate phenomena close to the reality. Grid computing is an answer to the growing demand of computational power. In a grid computing environment, communication times are significant and the bandwidth is variable, therefore frequent synchronizations slow down performances. Thus it is desirable to reduce the number of synchronizations in a parallel direct algorithm. Inspired from multisplitting techniques, the GREMLINS (GRid Efficient Methods for LINear Systems) solver we developed consists of solving several linear problems obtained by splitting. The principle of the balancing algorithm is presented, and experimental results are given.</Para></Abstract></ChapterHeader><NoBody></NoBody></Chapter></Part></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>A Sparse Linear System Solver Used in a Distributed and Heterogenous Grid Computing Environment</title></titleInfo><titleInfo type="alternative" contentType="CDATA"><title>A Sparse Linear System Solver Used in a Distributed and Heterogenous Grid Computing Environment</title></titleInfo><name type="personal"><namePart type="given">Christophe</namePart><namePart type="family">Denis</namePart><affiliation>School of Electronics Electrical Engineering & Computer Science, The Queen’s University of Belfast, Belfast BT7 1NN, UK</affiliation><affiliation>E-mail: C.Denis@qub.ac.uk</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Raphael</namePart><namePart type="family">Couturier</namePart><affiliation>Laboratoire d Informatique del Université de Franche-Comté, BP 527, 90016 Belfort Cedex, France</affiliation><affiliation>E-mail: Raphael.Couturier@iut-bm.univ-fcomte.fr</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Fabienne</namePart><namePart type="family">Jézéquel</namePart><affiliation>Laboratoire d’Informatique LIP6, UPMC Univ Paris 06, 4 place Jussieu, 75252 Paris Cedex 05, France</affiliation><affiliation>E-mail: Christophe.Denis@lip6.fr</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre displayLabel="OriginalPaper" authority="ISTEX" authorityURI="https://content-type.data.istex.fr" type="research-article" valueURI="https://content-type.data.istex.fr/ark:/67375/XTP-1JC4F85T-7">research-article</genre><originInfo><publisher>Springer New York</publisher><place><placeTerm type="text">New York, NY</placeTerm></place><dateIssued encoding="w3cdtf">2009</dateIssued><copyrightDate encoding="w3cdtf">2009</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><abstract lang="en">Abstract: Many scientific applications need to solve very large sparse linear systems in order to simulate phenomena close to the reality. Grid computing is an answer to the growing demand of computational power. In a grid computing environment, communication times are significant and the bandwidth is variable, therefore frequent synchronizations slow down performances. Thus it is desirable to reduce the number of synchronizations in a parallel direct algorithm. Inspired from multisplitting techniques, the GREMLINS (GRid Efficient Methods for LINear Systems) solver we developed consists of solving several linear problems obtained by splitting. The principle of the balancing algorithm is presented, and experimental results are given.</abstract><relatedItem type="host"><titleInfo><title>Parallel Scientific Computing and Optimization</title><subTitle>Advances and Applications</subTitle></titleInfo><name type="personal"><namePart type="given">Raimondas</namePart><namePart type="family">Čiegis</namePart><affiliation>Department of Mathematical Modelling, Vilnius Gediminas Technical University, Lithuania</affiliation><affiliation>E-mail: rc@fm.vgtu.lt</affiliation></name><name type="personal"><namePart type="given">David</namePart><namePart type="family">Henty</namePart><affiliation>University of Edinburgh, United Kingdom</affiliation><affiliation>E-mail: d.henty@epcc.ed.ac.uk</affiliation></name><name type="personal"><namePart type="given">Bo</namePart><namePart type="family">Kågström</namePart><affiliation>Umeå University, Sweden</affiliation><affiliation>E-mail: bokg@cs.umu.se</affiliation></name><name type="personal"><namePart type="given">Julius</namePart><namePart type="family">Žilinskas</namePart><affiliation>Vilnius Gediminas Technical University and Institute of Mathematics and Informatics, Lithuania</affiliation><affiliation>E-mail: julius.zilinskas@ktl.mii.lt</affiliation></name><genre type="book-series" authority="ISTEX" authorityURI="https://publication-type.data.istex.fr" valueURI="https://publication-type.data.istex.fr/ark:/67375/JMC-0G6R5W5T-Z">book-series</genre><originInfo><publisher>Springer</publisher><copyrightDate encoding="w3cdtf">2009</copyrightDate><issuance>monographic</issuance></originInfo><subject><genre>Book-Subject-Collection</genre><topic authority="SpringerSubjectCodes" authorityURI="SUCO11649">Mathematics and Statistics</topic></subject><subject><genre>Book-Subject-Group</genre><topic authority="SpringerSubjectCodes" authorityURI="SCM">Mathematics</topic><topic authority="SpringerSubjectCodes" authorityURI="SCM27004">Probability Theory and Stochastic Processes</topic><topic authority="SpringerSubjectCodes" authorityURI="SCI16005">Theory of Computation</topic><topic authority="SpringerSubjectCodes" authorityURI="SC521000">Operation Research/Decision Theory</topic><topic authority="SpringerSubjectCodes" authorityURI="SCM26024">Operations Research, Management Science</topic><topic authority="SpringerSubjectCodes" authorityURI="SCI21009">Computing Methodologies</topic><topic authority="SpringerSubjectCodes" authorityURI="SCM11094">Linear and Multilinear Algebras, Matrix Theory</topic></subject><identifier type="DOI">10.1007/978-0-387-09707-7</identifier><identifier type="ISBN">978-0-387-09706-0</identifier><identifier type="eISBN">978-0-387-09707-7</identifier><identifier type="ISSN">1931-6828</identifier><identifier type="BookTitleID">160109</identifier><identifier type="BookID">978-0-387-09707-7</identifier><identifier type="BookChapterCount">23</identifier><identifier type="BookVolumeNumber">27</identifier><identifier type="BookSequenceNumber">27</identifier><identifier type="PartChapterCount">5</identifier><part><date>2009</date><detail type="part"><title>Part I: Parallel Algorithms for Matrix Computations</title></detail><detail type="volume"><number>27</number><caption>vol.</caption></detail><extent unit="pages"><start>47</start><end>56</end></extent></part><recordInfo><recordOrigin>Springer New York, 2009</recordOrigin></recordInfo></relatedItem><relatedItem type="series"><titleInfo><title>Springer Optimization and Its Applications</title></titleInfo><name type="personal"><namePart type="given">Panos</namePart><namePart type="given">M.</namePart><namePart type="family">Pardalos</namePart><affiliation>University of Florida, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Ding-Zhu</namePart><namePart type="family">Du</namePart><affiliation>University of Texas, Dallas</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><originInfo><publisher>Springer</publisher><copyrightDate encoding="w3cdtf">2009</copyrightDate><issuance>serial</issuance></originInfo><identifier type="ISSN">1931-6828</identifier><identifier type="SeriesID">7393</identifier><part><detail type="volume"><number>Part I</number><caption>vol.</caption></detail></part><recordInfo><recordOrigin>Springer New York, 2009</recordOrigin></recordInfo></relatedItem><identifier type="istex">90324F2C50646B3170D5A953E452A01CACFB5BD6</identifier><identifier type="ark">ark:/67375/HCB-VNHCC148-T</identifier><identifier type="DOI">10.1007/978-0-387-09707-7_4</identifier><identifier type="ChapterID">4</identifier><identifier type="ChapterID">Chap4</identifier><accessCondition type="use and reproduction" contentType="copyright">Springer New York, 2009</accessCondition><recordInfo><recordContentSource authority="ISTEX" authorityURI="https://loaded-corpus.data.istex.fr" valueURI="https://loaded-corpus.data.istex.fr/ark:/67375/XBH-RLRX46XW-4">springer</recordContentSource><recordOrigin>Springer Science+Business Media, LLC, 2009</recordOrigin></recordInfo></mods><json:item><extension>json</extension><original>false</original><mimetype>application/json</mimetype><uri>https://api.istex.fr/ark:/67375/HCB-VNHCC148-T/record.json</uri></json:item></metadata><annexes><json:item><extension>txt</extension><original>true</original><mimetype>text/plain</mimetype><uri>https://api.istex.fr/ark:/67375/HCB-VNHCC148-T/annexes.txt</uri></json:item></annexes></istex></record>Pour manipuler ce document sous Unix (Dilib)
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