Polynomial and matrix computations (vol. 1): fundamental algorithms
Polynomial and matrix computations (vol. 1): fundamental algorithms
A parallel ring ordering algorithm for efficient one-sided Jacobi SVD computations
Journal of Parallel and Distributed Computing
Dynamic ordering for a parallel block-Jacobi SVD algorithm
Parallel Computing - Parallel matrix algorithms and applications
Memory hierarchy exploration for accelerating the parallel computation of SVDs
Neural, Parallel & Scientific Computations
Fast dimension reduction for document classification based on imprecise spectrum analysis
Information Sciences: an International Journal
A block JRS algorithm for highly parallel computation of SVDs
HPCC'07 Proceedings of the Third international conference on High Performance Computing and Communications
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The Singular Value Decomposition (SVD) is a vital problem that finds a place in numerous application domains in science and engineering. As an example, SVDs are used in processing voluminous data sets. Many sequential and parallel algorithms have been proposed to compute SVDs. The best known sequential algorithms take cubic time. This amount of time may not be acceptable especially when the data size is large. Thus parallel algorithms are desirable. In this paper, we present a novel technique for the parallel computation of SVDs. This technique yields impressive speedups. We discuss implementation of our technique on parallel models of computing such as the mesh and the PRAM. We also present an experimental evaluation of our technique.