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An hybrid approach for the parallelization of a block iterative algorithm

dc.contributor.authorBalsa, Carlos
dc.contributor.authorGuivarch, Ronan
dc.contributor.authorRuiz, Daniel
dc.contributor.authorZinadi, Mohamed
dc.date.accessioned2014-07-07T09:02:03Z
dc.date.available2014-07-07T09:02:03Z
dc.date.issued2011
dc.description.abstractThe Cimmino method is a row projection method in which the original linear system is divided into subsystems. At every iteration, it computes one projection per subsystem and uses these projections to construct an approximation to the solution of the linear system. The usual parallelization strategy in block algorithms is to distribute the different blocks on the available processors. In this paper, we follow another approach where we do not perform explicitly this block distribution to processors within the code, but let the multi-frontal sparse solver MUMPS handle the data distribution and parallelism. The data coming from the subsystems defined by the block partition in the Block Cimmino method are gathered in an unique block diagonal sparse matrix which is analysed, distributed and factorized in parallel by MUMPS. Our target is to define a methodology for parallelism based only on the functionalities provided by general sparse solver libraries and how efficient this way of doing can bepor
dc.identifier.citationBalsa, Carlos; Guivarch, Ronan; Ruiz, Daniel; Zinadi, Mohamed (2011). An hybrid approach for the parallelization of a block iterative algorithm. In High Performance Computing for Computational Science – VECPAR 2010. New York: Springer. p. 116-128. ISBN 978-3-642-19327-9por
dc.identifier.doi10.1007/978-3-642-19328-6_13
dc.identifier.isbn978-3-642-19327-9
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/10198/9848
dc.language.isoengpor
dc.peerreviewedyespor
dc.publisherSpringerpor
dc.subjectBlock cimmino methodpor
dc.subjectParallelpor
dc.subjectHybrid strategypor
dc.titleAn hybrid approach for the parallelization of a block iterative algorithmpor
dc.typeconference paper
dspace.entity.typePublication
oaire.citation.conferencePlaceNEW YORKpor
oaire.citation.endPage128por
oaire.citation.startPage116por
oaire.citation.titleHigh Performance Computing for Computational Science – VECPAR 2010por
oaire.citation.volume6449por
person.familyNameBalsa
person.givenNameCarlos
person.identifier1721518
person.identifier.ciencia-idDE1E-2F7A-AAB1
person.identifier.orcid0000-0003-2431-8665
person.identifier.ridM-8735-2013
person.identifier.scopus-author-id23391719100
rcaap.rightsrestrictedAccesspor
rcaap.typeconferenceObjectpor
relation.isAuthorOfPublicationd0e5ccff-9696-4f4f-9567-8d698a6bf17d
relation.isAuthorOfPublication.latestForDiscoveryd0e5ccff-9696-4f4f-9567-8d698a6bf17d

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