The generalized minimum residual (GMRES) method is a popular method for solving a large-scale sparse nonsymmetric linear system of equations. On modern computers, especially on a large-scale system, the communication is becoming increasingly expensive. To address this hardware trend, a communication-avoiding variant of GMRES (CA-GMRES) has become attractive, frequently showing its superior performance over GMRES on various hardware architectures. In practice, to mitigate the increasing costs of explicitly orthogonalizing the projection basis vectors, the iterations of both GMRES and CA-GMRES are restarted, which often slows down the solution convergence. To avoid this slowdown and improve the performance of restarted CA-GMRES, in this paper, we study the effectiveness of deflation strategies. Our studies are based on a thick restarted variant of CA-GMRES, which can implicitly deflate a few Ritz vectors, that approximately span an eigenspace of the coefficient matrix, through the standard orthogonalization process. This strategy is mathematically equivalent to the standard thick-restarted GMRES, and it requires only a small computational overhead and does not increase the communication or storage costs of CA-GMRES. Hence, by avoiding the communication, this deflated version of CA-GMRES obtains the same performance benefits over the deflated version of GMRES as the standard CA-GMRES does over GMRES. Our experimental results on a hybrid CPU/GPU cluster demonstrate that thick-restart can significantly improve the convergence and reduce the solution time of CA-GMRES. We also show that this deflation strategy can be combined with a local domain decomposition based preconditioner to further enhance the robustness of CA-GMRES, making it more attractive in practice.
['Ichitaro Yamazaki', 'Stanimire Tomov', 'Jack Dongarra']
Deflation strategies to improve the convergence of communication-avoiding GMRES