DC FieldValueLanguage
dc.contributor.authorMinion, Michael-
dc.contributor.authorSpeck, Robert-
dc.contributor.authorBolten, Matthias-
dc.contributor.authorEmmett, Matthew-
dc.contributor.authorRuprecht, Daniel-
dc.date.accessioned2021-10-14T10:16:19Z-
dc.date.available2021-10-14T10:16:19Z-
dc.date.issued2015-
dc.identifier.citationSIAM Journal on Scientific Computing 37(5), S244-S263 (2015)de_DE
dc.identifier.issn1064-8275de_DE
dc.identifier.urihttp://hdl.handle.net/11420/10524-
dc.description.abstractThe parallel full approximation scheme in space and time (PFASST) introduced by Emmett and Minion in 2012 is an iterative strategy for the temporal parallelization of ODEs and discretized PDEs. As the name suggests, PFASST is similar in spirit to a space-time FAS multigrid method performed over multiple time-steps in parallel. However, since the original focus of PFASST has been on the performance of the method in terms of time parallelism, the solution of any spatial system arising from the use of implicit or semi-implicit temporal methods within PFASST have simply been assumed to be solved to some desired accuracy completely at each sub-step and each iteration by some unspecified procedure. It hence is natural to investigate how iterative solvers in the spatial dimensions can be interwoven with the PFASST iterations and whether this strategy leads to a more efficient overall approach. This paper presents an initial investigation on the relative performance of different strategies for coupling PFASST iterations with multigrid methods for the implicit treatment of diffusion terms in PDEs. In particular, we compare full accuracy multigrid solves at each sub-step with a small fixed number of multigrid V-cycles. This reduces the cost of each PFASST iteration at the possible expense of a corresponding increase in the number of PFASST iterations needed for convergence. Parallel efficiency of the resulting methods is explored through numerical examples.en
dc.relation.ispartofSIAM journal on scientific computingde_DE
dc.subjectMultigridde_DE
dc.subjectParallel in timede_DE
dc.subjectPFASSTde_DE
dc.subjectMathematics - Numerical Analysisde_DE
dc.subjectMathematics - Numerical Analysisde_DE
dc.subjectComputer Science - Distributed; Parallel; and Cluster Computingde_DE
dc.subjectComputer Science - Numerical Analysisde_DE
dc.titleInterweaving PFASST and Parallel Multigridde_DE
dc.typeinProceedingsde_DE
dc.type.dinicontributionToPeriodical-
dcterms.DCMITypeText-
tuhh.abstract.englishThe parallel full approximation scheme in space and time (PFASST) introduced by Emmett and Minion in 2012 is an iterative strategy for the temporal parallelization of ODEs and discretized PDEs. As the name suggests, PFASST is similar in spirit to a space-time FAS multigrid method performed over multiple time-steps in parallel. However, since the original focus of PFASST has been on the performance of the method in terms of time parallelism, the solution of any spatial system arising from the use of implicit or semi-implicit temporal methods within PFASST have simply been assumed to be solved to some desired accuracy completely at each sub-step and each iteration by some unspecified procedure. It hence is natural to investigate how iterative solvers in the spatial dimensions can be interwoven with the PFASST iterations and whether this strategy leads to a more efficient overall approach. This paper presents an initial investigation on the relative performance of different strategies for coupling PFASST iterations with multigrid methods for the implicit treatment of diffusion terms in PDEs. In particular, we compare full accuracy multigrid solves at each sub-step with a small fixed number of multigrid V-cycles. This reduces the cost of each PFASST iteration at the possible expense of a corresponding increase in the number of PFASST iterations needed for convergence. Parallel efficiency of the resulting methods is explored through numerical examples.de_DE
tuhh.publisher.doi10.1137/14097536X-
tuhh.type.opusInProceedings (Aufsatz / Paper einer Konferenz etc.)-
dc.type.drivercontributionToPeriodical-
dc.type.casraiConference Paper-
tuhh.container.issue5de_DE
tuhh.container.volume37de_DE
tuhh.container.startpageS244de_DE
tuhh.container.endpageSS263de_DE
dc.identifier.arxiv1407.6486v2de_DE
dc.identifier.scopus2-s2.0-84928708654de_DE
local.publisher.peerreviewedtruede_DE
item.openairecristypehttp://purl.org/coar/resource_type/c_5794-
item.creatorOrcidMinion, Michael-
item.creatorOrcidSpeck, Robert-
item.creatorOrcidBolten, Matthias-
item.creatorOrcidEmmett, Matthew-
item.creatorOrcidRuprecht, Daniel-
item.cerifentitytypePublications-
item.mappedtypeinProceedings-
item.openairetypeinProceedings-
item.fulltextNo Fulltext-
item.grantfulltextnone-
item.creatorGNDMinion, Michael-
item.creatorGNDSpeck, Robert-
item.creatorGNDBolten, Matthias-
item.creatorGNDEmmett, Matthew-
item.creatorGNDRuprecht, Daniel-
crisitem.author.deptMathematik E-10-
crisitem.author.orcid0000-0002-3879-1210-
crisitem.author.orcid0000-0003-1904-2473-
crisitem.author.parentorgStudiendekanat Elektrotechnik, Informatik und Mathematik-
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