Parallel Performance of Algebraic Multigrid Domain Decomposition (AMG-DD)
Abstract
Algebraic multigrid (AMG) is a widely used scalable solver and preconditioner for large-scale linear systems resulting from the discretization of a wide class of elliptic PDEs. While AMG has optimal computational complexity, the cost of communication has become a significant bottleneck that limits its scalability as processor counts continue to grow on modern machines. This paper examines the design, implementation, and parallel performance of a novel algorithm, Algebraic Multigrid Domain Decomposition (AMG-DD), designed specifically to limit communication. The goal of AMG-DD is to provide a low-communication alternative to standard AMG V-cycles by trading some additional computational overhead for a significant reduction in communication cost. Numerical results show that AMG-DD achieves superior accuracy per communication cost compared to AMG, and speedup over AMG is demonstrated on a large GPU cluster.
Cite
@article{arxiv.1906.10575,
title = {Parallel Performance of Algebraic Multigrid Domain Decomposition (AMG-DD)},
author = {Wayne B. Mitchell and Robert Strzodka and Robert D. Falgout},
journal= {arXiv preprint arXiv:1906.10575},
year = {2020}
}