Parallel LU Factorization on GPU Cluster

This paper describes our progressindeveloping softwarefor performing parallelLUfactorizationofalarge 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 GPUdevices. Comparisonof the performancegainsversus the current ScaLAPACK PZGETRF are provided.