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2012.114: Implementing QR Factorization Updating Algorithms on GPUs

2012.114: Robert Andrew and Nicholas J. Dingle (2014) Implementing QR Factorization Updating Algorithms on GPUs. Parallel Computing, 4 (7). pp. 161-172. ISSN 0167-8191

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DOI: 10.1016/j.parco.2014.03.003

Abstract

Linear least squares problems are commonly solved by QR factorization. When multiple solutions need to be computed with only minor changes in the underlying data, knowledge of the difference between the old data set and the new can be used to update an existing factorization at reduced computational cost. We investigate the viability of implementing QR updating algorithms on GPUs and demonstrate that GPU-based updating for removing columns achieves speed-ups of up to 13.5x compared with full GPU QR factorization. We characterize the conditions under which other types of updates also achieve speed-ups.

Item Type:Article
Subjects:MSC 2000 > 15 Linear and multilinear algebra; matrix theory
MSC 2000 > 65 Numerical analysis
MSC 2000 > 68 Computer science
MIMS number:2012.114
Deposited By:Dr Nicholas Dingle
Deposited On:27 June 2014

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