Abstract
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.
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Rajasekaran, S., Song, M. (2006). A Novel Scheme for the Parallel Computation of SVDs. In: Gerndt, M., Kranzlmüller, D. (eds) High Performance Computing and Communications. HPCC 2006. Lecture Notes in Computer Science, vol 4208. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11847366_14
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DOI: https://doi.org/10.1007/11847366_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-39368-9
Online ISBN: 978-3-540-39372-6
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