Siegel, Howard Jay, authorMaciejewski, Anthony A., authorUlrey, Renard R., authorIEEE, publisher2007-01-032007-01-031994Ulrey, Renard R., Anthony A. Maciejewski, and Howard Jay Siegel, Parallel Algorithms for Singular Value Decomposition, Proceedings: Eighth International Parallel Processing Symposium, April 26-29, 1994, Cancún, Mexico: 524-533.http://hdl.handle.net/10217/1213In 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.born digitalproceedings (reports)eng©1994 IEEE.Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.parallel algorithmsmultiprocessor interconnection networksmatrix algebraparallel machinesperformance evaluationText