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Parallel matrix inversion techniques

Lau, K. K., Kumar, M. J. and Venkatesh, S. 1996, Parallel matrix inversion techniques, in ICAPP 1996 : IEEE International Conference on Algorithms and Architectures for Parallel Processing, IEEE, Piscataway, N. J., pp. 515-521.

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Title Parallel matrix inversion techniques
Author(s) Lau, K. K.
Kumar, M. J.
Venkatesh, S.
Conference name International Conference on Algorithms and Architectures for Parallel Processing (2nd : 1996 : Singapore, Singapore)
Conference location Singapore, Singapore
Conference dates 11-13 Jun. 1996
Title of proceedings ICAPP 1996 : IEEE International Conference on Algorithms and Architectures for Parallel Processing
Editor(s) [Unknown]
Publication date 1996
Conference series International Conference on Algorithms and Architectures for Parallel Processing
Start page 515
End page 521
Total pages 7
Publisher IEEE
Place of publication Piscataway, N. J.
Keyword(s) sparse matrices
matrix inversion
SIMD
MIMD
PVM
computer vision
snakes
Summary 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.
Notes This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
ISBN 0780335295
Language eng
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©1996, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044550

Document type: Conference Paper
Collections: School of Information Technology
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