期刊
JOURNAL OF COMPUTATIONAL PHYSICS
卷 304, 期 -, 页码 170-188出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jcp.2015.10.012
关键词
Fast direct solvers; Iterative solvers; Numerical linear algebra; Hierarchically off-diagonal low-rank matrices; Multifrontal elimination; Adaptive cross approximation
资金
- U.S. Army Research Laboratory, through the Army High Performance Computing Research Center [W911NF-07-0027]
- Department of Energy [DE-NA0002373-1]
This article presents a fast solver for the dense frontal matrices that arise from the multifrontal sparse elimination process of 3D elliptic PDEs. The solver relies on the fact that these matrices can be efficiently represented as a hierarchically off-diagonal low-rank (HODLR) matrix. To construct the low-rank approximation of the off-diagonal blocks, we propose a new pseudo-skeleton scheme, the boundary distance low-rank approximation, that picks rows and columns based on the location of their corresponding vertices in the sparse matrix graph. We compare this new low-rank approximation method to the adaptive cross approximation (ACA) algorithm and show that it achieves better speedup specially for unstructured meshes. Using the HODLR direct solver as a preconditioner (with a low tolerance) to the GMRES iterative scheme, we can reach machine accuracy much faster than a conventional LU solver. Numerical benchmarks are provided for frontal matrices arising from 3D finite element problems corresponding to a wide range of applications. (C) 2015 Elsevier Inc. All rights reserved.
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