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A QDWH-based SVD Software Framework on Distributed-memory Manycore Systems by Dalal Sukkari; Hatem Ltaief; Aniello Esposito; David Keyes is a Computer Science article available to read on EtoBox.

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This article presents a high-performance software framework for computing a dense SVD on distributed-memory manycore systems. Originally introduced by Nakatsukasa et al. (2010) and Nakatsukasa and Higham (2013), the SVD solver relies on the polar decomposition using the QR Dynamically Weighted Halley algorithm (QDWH). Although the QDWH-based SVD algorithm performs a significant amount of extra floating-point operations compared to the traditional SVD with the one-stage bidiagonal reduction, the inherent high level of concurrency associated with Level 3 BLAS compute-bound kernels ultimately compensates for the arithmetic complexity overhead. Using the ScaLAPACK two-dimensional block cyclic data distribution with a rectangular processor topology, the resulting QDWH-SVD further reduces excessive communications during the panel factorization, while increasing the degree of parallelism during the update of the trailing submatrix, as opposed to relying on the default square processor grid. After detailing the algorithmic complexity and the memory footprint of the algorithm, we conduct a thorough performance analysis and study the impact of the grid topology on the performance by looking

Who reads A QDWH-based SVD Software Framework on Distributed-memory Manycore Systems?

Author

Dalal Sukkari; Hatem Ltaief; Aniello Esposito; David Keyes

Publisher

ACM

Published

2019

Language

EN

Field

Computer Science (Physical Sciences)