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dc.contributor.authorBrown, Jed
dc.contributor.authorSmith, Barry
dc.contributor.authorAhmadia, Aron
dc.date.accessioned2015-05-25T08:29:14Z
dc.date.available2015-05-25T08:29:14Z
dc.date.issued2013-03-12
dc.identifier.citationAchieving Textbook Multigrid Efficiency for Hydrostatic Ice Sheet Flow 2013, 35 (2):B359 SIAM Journal on Scientific Computing
dc.identifier.issn1064-8275
dc.identifier.issn1095-7197
dc.identifier.doi10.1137/110834512
dc.identifier.urihttp://hdl.handle.net/10754/555665
dc.description.abstractThe hydrostatic equations for ice sheet flow offer improved fidelity compared with the shallow ice approximation and shallow stream approximation popular in today's ice sheet models. Nevertheless, they present a serious bottleneck because they require the solution of a three-dimensional (3D) nonlinear system, as opposed to the two-dimensional system present in the shallow stream approximation. This 3D system is posed on high-aspect domains with strong anisotropy and variation in coefficients, making it expensive to solve with current methods. This paper presents a Newton--Krylov multigrid solver for the hydrostatic equations that demonstrates textbook multigrid efficiency (an order of magnitude reduction in residual per iteration and solution of the fine-level system at a small multiple of the cost of a residual evaluation). Scalability on Blue Gene/P is demonstrated, and the method is compared to various algebraic methods that are in use or have been proposed as viable approaches.
dc.publisherSociety for Industrial & Applied Mathematics (SIAM)
dc.relation.urlhttp://epubs.siam.org/doi/abs/10.1137/110834512
dc.rightsArchived with thanks to SIAM Journal on Scientific Computing
dc.subjecthydrostatic
dc.subjectice sheet
dc.subjectpreconditioning
dc.subjectNewton--Krylov
dc.subjectmultigrid
dc.titleAchieving Textbook Multigrid Efficiency for Hydrostatic Ice Sheet Flow
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentKAUST Supercomputing Laboratory (KSL)
dc.identifier.journalSIAM Journal on Scientific Computing
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionMathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL 60439
kaust.personAhmadia, Aron
refterms.dateFOA2018-06-14T07:54:56Z
dc.date.published-online2013-03-12
dc.date.published-print2013-01


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