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Parallel matrix inversion techniques

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3 Author(s)
Lau, K.K. ; Dept. of Comput. Sci., Curtin Univ. of Technol., Bentley, WA, Australia ; Kumar, M.J. ; Venkatesh, S.

In this paper, we present techniques for inverting sparse, symmetric and positive definite matrices on parallel and distributed computers. We propose two algorithms, one for SIMD implementation and the other for MIMD implementation. These algorithms are modified versions of Gaussian elimination and they take into account the sparseness of the matrix. Our algorithms perform better than the general parallel Gaussian elimination algorithm. In order to demonstrate the usefulness of our technique, we implemented the snake problem using our sparse matrix algorithm. Our studies reveal that the proposed sparse matrix inversion algorithm significantly reduces the time taken for obtaining the solution of the snake problem. In this paper, we present the results of our experimental work

Published in:

Algorithms & Architectures for Parallel Processing, 1996. ICAPP 96. 1996 IEEE Second International Conference on

Date of Conference:

11-13 Jun 1996