High-Performance Pseudo-Random Number Generation on Graphics Processing Units
Abstract
This work considers the deployment of pseudo-random number generators (PRNGs) on graphics processing units (GPUs), developing an approach based on the xorgens generator to rapidly produce pseudo-random numbers of high statistical quality. The chosen algorithm has configurable state size and period, making it ideal for tuning to the GPU architecture. We present a comparison of both speed and statistical quality with other common parallel, GPU-based PRNGs, demonstrating favourable performance of the xorgens-based approach.
- Publication:
-
arXiv e-prints
- Pub Date:
- August 2011
- DOI:
- 10.48550/arXiv.1108.0486
- arXiv:
- arXiv:1108.0486
- Bibcode:
- 2011arXiv1108.0486N
- Keywords:
-
- Computer Science - Distributed;
- Parallel;
- and Cluster Computing;
- Mathematics - Number Theory;
- Statistics - Computation;
- 11K45 (Primary) 65C10;
- 65Y05;
- 65Y10 (Secondary);
- D.1.3;
- G.3;
- G.4;
- I.6.8
- E-Print:
- 10 pages, submitted to PPAM 2011 (Torun, Poland, 11-14 Sept. 2011). For further information, see http://maths.anu.edu.au/~brent/pub/pub241.html