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SCHEDULE: NOV 15-20, 2015

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A Splitting Approach for the Parallel Solution of Large Linear Systems on GPU Cards

SESSION: Regular & ACM Student Research Competition Poster Reception

EVENT TYPE: Posters, Receptions, ACM Student Research Competition

EVENT TAG(S): HPC Beginner Friendly, Regular Poster

TIME: 5:15PM - 7:00PM

SESSION CHAIR(S): Michela Becchi, Manish Parashar, Dorian C. Arnold

AUTHOR(S):Ang Li, Radu Serban, Dan Negrut

ROOM:Level 4 - Lobby


We discuss a GPU solver for sparse or dense banded linear systems Ax=b, with A possibly nonsymmetric, sparse, and moderately large. The split and parallelize (SaP) approach seeks to partition the matrix A into P diagonal sub-blocks which are independently factored in parallel. The solution may choose to consider or to ignore the off-diagonal coupling blocks. This approach, along with the Krylov iterative methods that it preconditions, are implemented in the SaP::GPU solver, which runs entirely on the GPU except for several stages involved in preliminary row-column permutations.

SaP::GPU compares well in terms of efficiency with three commonly used sparse direct solvers: PARDISO, SuperLU, and MUMPS. Compared to Intel's MKL, SaP::GPU proves to also be performant on dense banded systems that are close to being diagonally dominant.

SaP::GPU is available open source under a permissive BSD3 license.

Chair/Author Details:

Michela Becchi, Manish Parashar, Dorian C. Arnold (Chair) - University of Missouri|Rutgers University|University of New Mexico|

Ang Li - University of Wisconsin-Madison

Radu Serban - University of Wisconsin-Madison

Dan Negrut - University of Wisconsin-Madison

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