Parallel LU Factorization on GPU cluster

Abstract

This paper describes our progress in developing software for performing parallel LU factorization of a large dense matrix on a GPU cluster. Three approaches, with increasing software complexity, are considered: (i) a naive 'thunking' approach that links the existing parallel ScaLAPACK software library with cuBLAS through a software emulation layer; (ii) a more intrusive magmaBLAS implementation integrated into the LU solver in the High-Performance Linpack software; and (iii) a left-looking out-of-core algorithm for solving problems that are larger than the available memory on GPU devices. Comparison of the performance gains versus the current ScaLAPACK PZGETRF are provided.