Self-Adaptive Multiprecision Preconditioners on Multicore and Manycore Architectures
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}
}
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.
April 27, 2014 by hgpu
