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Self-Adaptive Multiprecision Preconditioners on Multicore and Manycore Architectures

Hartwig Anzt, Dimitar Lukarski, Stanimire Tomov, Jack Dongarra
Innovative Computing Lab, University of Tennessee, Knoxville, USA
University of Tennessee, Knoxville, Preprint ut-eecs-14-728, 2014

@article{anzt2014self,

   title={Self-Adaptive Multiprecision Preconditioners on Multicore and Manycore Architectures},

   author={Anzt, Hartwig and Lukarski, Dimitar and Tomov, Stanimire and Dongarra, Jack},

   year={2014}

}

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Based on the premise that preconditioners needed for scientific computing are not only required to be robust in the numerical sense, but also scalable for up to thousands of light-weight cores, we argue that this two-fold goal is achieved for the recently developed self-adaptive multi-elimination preconditioner. For this purpose, we revise the underlying idea and analyze the performance of implementations realized in the PARALUTION and MAGMA open-source software libraries on GPU architectures (using either CUDA or OpenCL), Intel’s Many Integrated Core Architecture, and Intel’s Sandy Bridge processor. The comparison with other well-established preconditioners like multi-coloured Gauss-Seidel, ILU(0) and multi-colored ILU(0), shows that the twofold goal of a numerically stable cross-platform performant algorithm is achieved.
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