Parrallel Implementation of Fast Randomized Algorithms for Low Rank Matrix Decomposition

Journal Article · Sat Mar 01 00:00:00 EST 2014 · Parallel Processing Letters, 24(1):Article No. 1450004

DOI:https://doi.org/10.1142/S0129626414500042· OSTI ID:1214081

Lucas, Andrew J.; Stalizer, Mark; Feo, John T.

We analyze the parallel performance of randomized interpolative decomposition by de- composing low rank complex-valued Gaussian random matrices larger than 100 GB. We chose a Cray XMT supercomputer as it provides an almost ideal PRAM model permitting quick investigation of parallel algorithms without obfuscation from hardware idiosyncrasies. We obtain that on non-square matrices performance scales almost linearly with runtime about 100 times faster on 128 processors. We also verify that numerically discovered error bounds still hold on matrices two orders of magnitude larger than those previously tested.

• Research Organization: Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 1214081
• Report Number(s): PNNL-SA-91896; 400470000
• Journal Information: Parallel Processing Letters, 24(1):Article No. 1450004, Journal Name: Parallel Processing Letters, 24(1):Article No. 1450004
• Country of Publication: United States
• Language: English