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
This is the latest version of this eprint.
Full text available as:
|PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader|
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.
|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:||27 June 2014|
Available Versions of this Item
- Implementing QR Factorization Updating Algorithms on GPUs (deposited 27 June 2014) [Currently Displayed]