Sparse approximate inverse preconditioners on high performance GPU platforms

Show simple item record Bertaccini, Daniele Filippone, Salvatore 2017-02-08T12:25:39Z 2017-02-08T12:25:39Z 2016-01-28
dc.identifier.citation Daniele Bertaccini, Salvatore Filippone, Sparse approximate inverse preconditioners on high performance GPU platforms, Computers & Mathematics with Applications, Volume 71, Issue 3, February 2016, pp693-711 en_UK
dc.identifier.issn 0898-1221
dc.description.abstract Simulation with models based on partial differential equations often requires the solution of (sequences of) large and sparse algebraic linear systems. In multidimensional domains, preconditioned Krylov iterative solvers are often appropriate for these duties. Therefore, the search for efficient preconditioners for Krylov subspace methods is a crucial theme. Recent developments, especially in computing hardware, have renewed the interest in approximate inverse preconditioners in factorized form, because their application during the solution process can be more efficient. We present here some experiences focused on the approximate inverse preconditioners proposed by Benzi and Tůma from 1996 and the sparsification and inversion proposed by van Duin in 1999. Computational costs, reorderings and implementation issues are considered both on conventional and innovative computing architectures like Graphics Programming Units (GPUs). en_UK
dc.language.iso en en_UK
dc.publisher Elsevier en_UK
dc.rights Attribution-Non-Commercial-No Derivatives 3.0 Unported (CC BY-NC-ND 3.0). You are free to: Share — copy and redistribute the material in any medium or format. The licensor cannot revoke these freedoms as long as you follow the license terms. Under the following terms: Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. Information: Non-Commercial — You may not use the material for commercial purposes. No Derivatives — If you remix, transform, or build upon the material, you may not distribute the modified material. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
dc.subject Preconditioners en_UK
dc.subject Approximate inverses en_UK
dc.subject Sparse matrices en_UK
dc.subject GPU en_UK
dc.title Sparse approximate inverse preconditioners on high performance GPU platforms en_UK
dc.type Article en_UK

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