Distributed Southwell: An Iterative Method with Low Communication Costs

Abstract

We present a new algorithm, the Distributed Southwell method, as a competitor to Block Jacobi for preconditioning and multi-grid smoothing. It is based on the Southwell iterative method, which is sequential, where only the equation with the largest residual is relaxed per iteration. The Parallel Southwell method extends this idea by relaxing equation i if it has the largest residual among all the equations coupled to variable i. Since communication is required for processes to exchange residuals, this method in distributed memory can be expensive. Distributed Southwell uses a novel scheme to reduce this communication of residuals while avoiding deadlock. Using test problems from the SuiteSparse Matrix Collection, we show that Distributed Southwell requires less communication to reach the same accuracy when compared to Parallel Southwell. Additionally, we show that the convergence of Distributed Southwell does not degrade like that of Block Jacobi when the number of processes is increased.