Journal
JOURNAL OF COMPUTATIONAL PHYSICS
Volume 219, Issue 1, Pages 198-209Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jcp.2006.03.031
Keywords
sparse matrix; preconditioning; chemical physics; parallel computing; eigensolver; linear solver
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Linear systems in chemical physics often involve matrices. with a certain sparse block structure. These can often be solved very effectively using iterative methods (sequence of matrix-vector products) in conjunction with a block Jacobi preconditioner [B. Poirier, Numer. Linear Algebra Appl. 7 (2000) 715]. In a two-part series, we present an efficient parallel implementation, incorporating several additional refinements. The present study (paper II) indicates that the basic parallel sparse matrix-vector product operation itself is the overall scalability bottleneck, faring much more poorly than the specialized, block Jacobi routines considered in a companion paper (paper I). However, a simple dimensional combination scheme is found to alleviate this difficulty. (c) 2006 Elsevier Inc. All rights reserved.
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