2012.114: Implementing QR Factorization Updating Algorithms on GPUs
2012.114: Robert Andrew and Nicholas J. Dingle (2012) Implementing QR Factorization Updating Algorithms on GPUs.
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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 2000 > 15 Linear and multilinear algebra; matrix theory|
MSC 2000 > 65 Numerical analysis
MSC 2000 > 68 Computer science
|Deposited By:||Dr Nicholas Dingle|
|Deposited On:||02 December 2012|
Available Versions of this Item
- Implementing QR Factorization Updating Algorithms on GPUs (deposited 27 June 2014)
- Implementing QR Factorization Updating Algorithms on GPUs (deposited 28 March 2014)
- Implementing QR Factorization Updating Algorithms on GPUs (deposited 02 December 2012) [Currently Displayed]