Multifrontal computations on GPUs and their multi-core hosts

Authors:
Robert F. Lucas;Gene Wagenbreth;Dan M. Davis;Roger Grimes
Affiliations:
Information Sciences Institute, University of Southern California, Marina del Rey, California;Information Sciences Institute, University of Southern California, Marina del Rey, California;Information Sciences Institute, University of Southern California, Marina del Rey, California;Livermore Software Technology Corporation, Livermore, California
Venue:
VECPAR'10 Proceedings of the 9th international conference on High performance computing for computational science
Year:
2010

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Abstract

The use of GPUs to accelerate the factoring of large sparse symmetric matrices shows the potential of yielding important benefits to a large group of widely used applications. This paper examines how a multifrontal sparse solver performs when exploiting both the GPU and its multi-core host. It demonstrates that the GPU can dramatically accelerate the solver relative to one host CPU. Furthermore, the solver can profitably exploit both the GPU to factor its larger frontal matrices and multiple threads on the host to handle the smaller frontal matrices.