Implementing QR Factorization Updating Algorithms on GPUs

Andrew, Robert and Dingle, Nicholas J. (2012) Implementing QR Factorization Updating Algorithms on GPUs. [MIMS Preprint]

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Abstract

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

Item Type:	MIMS Preprint
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:	02 Dec 2012
Last Modified:	08 Nov 2017 18:18
URI:	https://eprints.maths.manchester.ac.uk/id/eprint/1924

Available Versions of this Item

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

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