Multifrontal computations on GPUs and their multi-core hosts

Robert F. Lucas, Gene Wagenbreth, Dan M. Davis, Roger Grimes
Information Sciences Institute, University of Southern California, 4676 Admiralty Way, Suite 1001, Marina del Rey, California 90230
High Performance Computing for Computational Science – VECPAR 2010, Lecture Notes in Computer Science, Volume 6449/2011, 71-82, 2011


   title={Multifrontal computations on GPUs and their multi-core hosts},

   author={Lucas, R. and Wagenbreth, G. and Davis, D. and Grimes, R.},

   journal={High Performance Computing for Computational Science–VECPAR 2010},





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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.
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