venkatesh-parallelmatrix-1996.pdf (588.16 kB)
Parallel matrix inversion techniques
conference contribution
posted on 1996-01-01, 00:00 authored by K Lau, M Kumar, Svetha VenkateshSvetha VenkateshIn 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.
History
Event
International Conference on Algorithms and Architectures for Parallel Processing (2nd : 1996 : Singapore, Singapore)Pagination
515 - 521Publisher
IEEELocation
Singapore, SingaporePlace of publication
Piscataway, N. J.Publisher DOI
Start date
1996-06-11End date
1996-06-13ISBN-10
0780335295Language
engNotes
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E1.1 Full written paper - refereedCopyright notice
1996, IEEETitle of proceedings
ICAPP 1996 : IEEE International Conference on Algorithms and Architectures for Parallel ProcessingUsage metrics
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