Scalable Hierarchical Algorithms for stochastic PDEs and UQ
dc.contributor.author | Litvinenko, Alexander | |
dc.contributor.author | Chavez Chavez, Gustavo Ivan | |
dc.contributor.author | Keyes, David E. | |
dc.contributor.author | Ltaief, Hatem | |
dc.contributor.author | Yokota, Rio | |
dc.date.accessioned | 2017-06-05T08:35:47Z | |
dc.date.available | 2017-06-05T08:35:47Z | |
dc.date.issued | 2015-01-07 | |
dc.identifier.uri | http://hdl.handle.net/10754/624058 | |
dc.description.abstract | H-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.title | Scalable Hierarchical Algorithms for stochastic PDEs and UQ | |
dc.type | Poster | |
dc.contributor.department | Applied Mathematics and Computational Science Program | |
dc.contributor.department | Computer Science Program | |
dc.contributor.department | Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division | |
dc.contributor.department | Extreme Computing Research Center | |
dc.conference.date | January 6-9, 2015 | |
dc.conference.name | Advances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2015) | |
dc.conference.location | KAUST | |
kaust.person | Litvinenko, Alexander | |
kaust.person | Chavez Chavez, Gustavo Ivan | |
kaust.person | Keyes, David E. | |
kaust.person | Ltaief, Hatem | |
kaust.person | Yokota, Rio | |
refterms.dateFOA | 2018-06-14T04:26:16Z |
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Applied Mathematics and Computational Science Program
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Extreme Computing Research Center
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Computer Science Program
For more information visit: https://cemse.kaust.edu.sa/cs -
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
For more information visit: https://cemse.kaust.edu.sa/ -
Conference on Advances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2015)