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dc.contributor.authorAdams, Mark F.
dc.contributor.authorBrown, Jed
dc.contributor.authorKnepley, Matt
dc.contributor.authorSamtaney, Ravi
dc.date.accessioned2016-11-30T13:53:16Z
dc.date.available2016-11-30T13:53:16Z
dc.date.issued2016-08-04
dc.identifier.citationAdams MF, Brown J, Knepley M, Samtaney R (2016) Segmental Refinement: A Multigrid Technique for Data Locality. SIAM Journal on Scientific Computing 38: C426–C440. Available: http://dx.doi.org/10.1137/140975127.
dc.identifier.issn1064-8275
dc.identifier.issn1095-7197
dc.identifier.doi10.1137/140975127
dc.identifier.urihttp://hdl.handle.net/10754/621908
dc.description.abstractWe investigate a domain decomposed multigrid technique, termed segmental refinement, for solving general nonlinear elliptic boundary value problems. We extend the method first proposed in 1994 by analytically and experimentally investigating its complexity. We confirm that communication of traditional parallel multigrid is eliminated on fine grids, with modest amounts of extra work and storage, while maintaining the asymptotic exactness of full multigrid. We observe an accuracy dependence on the segmental refinement subdomain size, which was not considered in the original analysis. We present a communication complexity analysis that quantifies the communication costs ameliorated by segmental refinement and report performance results with up to 64K cores on a Cray XC30.
dc.description.sponsorshipThis material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, and performed under the auspices of the U.S. Department of Energy by Lawrence Berkeley National Laboratory under contract DE-AC02-05CH11231. This research used resources of the National Energy Research Scientific Computing Center, which is a DOE Office of Science User Facility. Authors from Lawrence Berkeley National Laboratory were supported by the U.S. Department of Energy's Advanced Scientific Computing Research Program under contract DEAC02-05CH11231.
dc.publisherSociety for Industrial & Applied Mathematics (SIAM)
dc.relation.urlhttp://epubs.siam.org/doi/10.1137/140975127
dc.rightsArchived with thanks to SIAM Journal on Scientific Computing
dc.subjectmultigrid
dc.subjectparallel multigrid
dc.subjectdistributed memory multigrid
dc.subjectsegmental refinement
dc.titleSegmental Refinement: A Multigrid Technique for Data Locality
dc.typeArticle
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Division
dc.contributor.departmentMechanical Engineering Program
dc.identifier.journalSIAM Journal on Scientific Computing
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionScalable Solvers Group, Lawrence Berkeley Laboratory, Berkeley, CA, United States
dc.contributor.institutionDepartment of Computer Science, University of Colorado, Boulder, CO, United States
dc.contributor.institutionComputational and Applied Mathematics, Rice University, Houston, TX, United States
kaust.personSamtaney, Ravi
refterms.dateFOA2018-06-13T15:26:12Z


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