Parallel sparse linear solver GMRES for GPU clusters with compression of exchanged data

Abstract

GPU clusters have become attractive parallel platforms for high performance computing due to their ability to compute faster than the CPU clusters. We use this architecture to accelerate the mathematical operations of the GMRES method for solving large sparse linear systems. However the parallel sparse matrix-vector product of GMRES causes overheads in CPU/CPU and GPU/CPU communications when exchanging large shared vectors of unknowns between GPUs of the cluster. Since a sparse matrix-vector product does not often need all the unknowns of the vector, we propose to use data compression and decompression operations on the shared vectors, in order to exchange only the needed unknowns. In this paper we present a new parallel GMRES algorithm for GPU clusters, using compression vectors. Our experimental results show that the GMRES solver is more efficient when using the data compression technique on large shared vectors. © 2012 Springer-Verlag Berlin Heidelberg.