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dc.contributor.authorLitvinenko, Alexander
dc.contributor.authorChavez Chavez, Gustavo Ivan
dc.contributor.authorKeyes, David E.
dc.contributor.authorLtaief, Hatem
dc.contributor.authorYokota, Rio
dc.date.accessioned2017-05-16T08:22:18Z
dc.date.available2017-05-16T08:22:18Z
dc.date.issued2015-01-05
dc.identifier.urihttp://hdl.handle.net/10754/623624
dc.description.abstractH-matrices and Fast Multipole (FMM) are powerful methods to approximate linear operators coming from partial differential and integral equations as well as speed up computational cost from quadratic or cubic to log-linear (O(n log n)), where n number of degrees of freedom in the discretization. The storage is reduced to the log-linear as well. This hierarchical structure is a good starting point for parallel algorithms. Parallelization on shared and distributed memory systems was pioneered by R. Kriemann, 2005. Since 2005, the area of parallel architectures and software is developing very fast. Progress in GPUs and Many-Core Systems (e.g. XeonPhi with 64 cores) motivated us to extend work started in [1,2,7,8].
dc.description.sponsorshipSRI UQ KAUST
dc.relation.urlhttps://sri-uq.kaust.edu.sa/Pages/UQAnnualWorkshop2015.aspx
dc.subjectscalable algorithms
dc.subjecthierarchical matrices
dc.subjectUncertainty Quantification
dc.subjectlarge covariance matrices
dc.subjectHLIB
dc.subjectHLIBPro
dc.titleScalable Hierarchical Algorithms for stochastic PDEs and Uncertainty Quantification
dc.typePoster
dc.contributor.departmentSRI Uncertainty Quantification Center
dc.contributor.departmentExtreme Computing Research Center
dc.conference.dateJanuary 2015
dc.conference.nameAdvances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2015)
dc.conference.locationKAUST, B1
refterms.dateFOA2018-06-13T15:30:48Z


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Scalable algoruthms, Hierarchical matrices, for uncertainty quantification andfor approximating large covariance matrices

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