|Title||Self-Adaptive Multiprecision Preconditioners on Multicore and Manycore Architectures|
|Publication Type||Conference Paper|
|Year of Publication||2014|
|Authors||Anzt, H., D. Lukarski, S. Tomov, and J. Dongarra|
|Conference Name||VECPAR 2014|
|Conference Location||Eugene, OR|
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.
Self-Adaptive Multiprecision Preconditioners on Multicore and Manycore Architectures