Sparse Communication Avoiding Pivoting and GPUs

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Persistent URL	http://purl.org/net/epubs/work/12417475
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Record Id	12417475
Title	Sparse Communication Avoiding Pivoting and GPUs
Contributors	Dr JD Hogg (STFC Rutherford Appleton Lab.)
Abstract	Modern GPUs provide a window on future hardware platforms that we need to design for. In this talk we describe our experience in implementing direct methods for sparse symmetric indefinite matrices in CUDA, a topic that presents both mathematical and technical challenges. Whilst a number of communication-avoiding methods have been developed for dense linear algebra, their applicability to sparse linear algebra is limited as they ignore sparsity in selecting pivots, generating significant fill. We will describe heuristics that attempt to manage this complexity through a series of fall-back strategies that in the best case have similar communication properties to the unpivoted Cholesky factorization, but for numerically challenging problems deliver stability on a par with the best existing sparse symmetric indefinite direct solvers.
Organisation	STFC , SCI-COMP , SCI-COMP-CM
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Language	English (EN)

Type	Details	URI(s)	Local file(s)	Year
Presentation	Presented at SIAM Computational Science and Engineering 2015 (CSE15), Salt Lake City, Utah, USA, 13-18 Mar 2015.		sps_ca_piv.pdf	2015