Parallel algorithms for singular value decomposition
Date
1994
Authors
Siegel, Howard Jay, author
Maciejewski, Anthony A., author
Ulrey, Renard R., author
IEEE, publisher
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Abstract
In motion rate control applications, it is faster and easier to solve the equations involved if the singular value decomposition (SVD) of the Jacobian matrix is first determined. A parallel SVD algorithm with minimum execution time is desired. One approach using Givens rotations lends itself to parallelization, reduces the iterative nature of the algorithm, and efficiently handles rectangular matrices. This research focuses on the minimization of the SVD execution time when using this approach. Specific issues addressed include considerations of data mapping, effects of the number of processors used on execution time, impacts of the interconnection network on performance, and trade-offs between modes of parallelism. Results are verified by experimental data collected on the PASM parallel machine prototype.
Description
Rights Access
Subject
parallel algorithms
multiprocessor interconnection networks
matrix algebra
parallel machines
performance evaluation