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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.accessioned2017-06-05T08:35:47Z
dc.date.available2017-06-05T08:35:47Z
dc.date.issued2015-01-07
dc.identifier.urihttp://hdl.handle.net/10754/624058
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 Kriemann [1,2]. 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.titleScalable Hierarchical Algorithms for stochastic PDEs and UQ
dc.typePoster
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentExtreme Computing Research Center
dc.conference.dateJanuary 6-9, 2015
dc.conference.nameAdvances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2015)
dc.conference.locationKAUST
kaust.personLitvinenko, Alexander
kaust.personChavez Chavez, Gustavo Ivan
kaust.personKeyes, David E.
kaust.personLtaief, Hatem
kaust.personYokota, Rio
refterms.dateFOA2018-06-14T04:26:16Z


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