ISSN:
1573-7691
Keywords:
Domain decomposition
;
nested dissection
;
LU-factorization
;
parallel computers
;
MIMD
Source:
Springer Online Journal Archives 1860-2000
Topics:
Computer Science
Notes:
Abstract A multilevel algorithm is presented for direct, parallel factorization of the large sparse matrices that arise from finite element and spectral element discretization of elliptic partial differential equations. Incomplete nested dissection and domain decomposition are used to distribute the domain among the processors and to organize the matrix into sections in which pivoting is applied to stabilize the factorization of indefinite equation sets. The algorithm is highly parallel and memory efficient; the efficient use of sparsity in the matrix allows the solution of larger problems as the number of processors is increased, and minimizes computations as well as the number and volume of communications among the processors. The number of messages and the total volume of messages passed during factorization, which are used as measures of algorithm efficiency, are reduced significantly compared to other algorithms. Factorization times are low and speedups high for implementation on an Intel iPSC/860 hypercube computer. Furthermore, the timings for forward and back substitutions are more than an order-of-magnitude smaller than the matrix decomposition times.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1007/BF01061145
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