Scalability of k -Tridiagonal Matrix Singular Value Decomposition

Andrei Tănăsescu, Mihai Carabaş, Florin Pop and Pantelimon George Popescu
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Andrei Tănăsescu: Computer Science and Engineering Department, Faculty of Automatic Control and Computer Science, University Politehnica of Bucharest, Splaiul Independentei 313, 060042 Bucharest, Romania
Mihai Carabaş: Computer Science and Engineering Department, Faculty of Automatic Control and Computer Science, University Politehnica of Bucharest, Splaiul Independentei 313, 060042 Bucharest, Romania
Florin Pop: Computer Science and Engineering Department, Faculty of Automatic Control and Computer Science, University Politehnica of Bucharest, Splaiul Independentei 313, 060042 Bucharest, Romania
Pantelimon George Popescu: Computer Science and Engineering Department, Faculty of Automatic Control and Computer Science, University Politehnica of Bucharest, Splaiul Independentei 313, 060042 Bucharest, Romania

Mathematics, 2021, vol. 9, issue 23, 1-11

Abstract: Singular value decomposition has recently seen a great theoretical improvement for k -tridiagonal matrices, obtaining a considerable speed up over all previous implementations, but at the cost of not ordering the singular values. We provide here a refinement of this method, proving that reordering singular values does not affect performance. We complement our refinement with a scalability study on a real physical cluster setup, offering surprising results. Thus, this method provides a major step up over standard industry implementations.

Keywords: scalability; k -tridiagonal matrix; singular value decomposition; LAPACK (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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