Fast Cholesky Factorization on GPUs for Batch and Native Modes in MAGMA

TitleFast Cholesky Factorization on GPUs for Batch and Native Modes in MAGMA
Publication TypeJournal Article
Year of Publication2017
AuthorsAbdelfattah, A., A. Haidar, S. Tomov, and J. Dongarra
JournalJournal of Computational Science
Date Published2017-05
KeywordsGPU computing; Cholesky factorization; Batched execution

This paper presents a GPU-accelerated Cholesky factorization for two different modes of operation. The first one is the batch mode, where many independent factorizations on small matrices can be performed concurrently. This mode supports fixed size and variable size problems, and is found in many scientific applications. The second mode is the native mode, where one factorization is performed on a large matrix without any CPU involvement, which allows the CPU do other useful work. We show that, despite the different workloads, both modes of operation share a common code-base that uses the GPU only. We also show that the developed routines achieve significant speedups against a multicore CPU using the MKL library, and against a GPU implementation by cuSOLVER. This work is part of the MAGMA library.

Project Tags: 
External Publication Flag: