sponsored byIEEEACMThe International Conference for High Performance 
Computing, Networking, Storage and Analysis
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SCHEDULE: NOV 16-21, 2014

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GPU Acceleration of Small Dense Matrix Computation of the One-Sided Factorizations

SESSION: Poster Reception


TIME: 5:15PM - 7:00PM

AUTHOR(S):Tingxing Dong, Mark Gates, Azzam Haidar, Piotr Luszczek, Stanimire Tomov

ROOM:New Orleans Theater Lobby


In scientific of applications, one often needs to solve many small size problems. The size of each of these small linear systems depends, for example, on the number of the ordinary differential equations (ODEs) used in the model, and can be on the order of hundreds of unknowns. To efficiently exploit the computing power of modern accelerator hardware, these linear systems are processed in batches.The state-of-the-art libraries for linear algebra that target GPUs, such as MAGMA, focus on large matrix sizes. They change the data layout by transposing the matrix to avoid these divergence and non-coalescing penalties. However, the data movement associated with transposition is very expensive for small matrices. We propose a batched one-sided factorizations for GPUs by using a multi-level blocked right looking algorithm that preserves the data layout but minimizes the penalty of partial pivoting. Our implementation achieves many-fold speedup when compared to the alternatives.

Chair/Author Details:

Tingxing Dong - University of Tennessee, Knoxville

Mark Gates - University of Tennessee, Knoxville

Azzam Haidar - University of Tennessee, Knoxville

Piotr Luszczek - University of Tennessee, Knoxville

Stanimire Tomov - University of Tennessee, Knoxville

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