Implementing QR Factorization Updating Algorithms on GPUs

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

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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 2010, the AMS's Mathematics Subject Classification > 15 Linear and multilinear algebra; matrix theory MSC 2010, the AMS's Mathematics Subject Classification > 65 Numerical analysis MSC 2010, the AMS's Mathematics Subject Classification > 68 Computer science
Depositing User:	Dr Nicholas Dingle
Date Deposited:	27 Jun 2014
Last Modified:	20 Oct 2017 14:13
URI:	https://eprints.maths.manchester.ac.uk/id/eprint/2152

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• Implementing QR Factorization Updating Algorithms on GPUs. (deposited 02 Dec 2012)
  • Implementing QR Factorization Updating Algorithms on GPUs. (deposited 28 Mar 2014)
    • Implementing QR Factorization Updating Algorithms on GPUs. (deposited 27 Jun 2014) [Currently Displayed]

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