Self-Adaptive Multiprecision Preconditioners on Multicore and Manycore Architectures
Hartwig Anzt and Dimitar Lukarski and Stanimire Tomov and Jack Dongarra
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 under-lying 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.
Published 2014-04-15 04:00:00 as ut-eecs-14-728 (ID:586)