publication . Part of book or chapter of book . Preprint . 2011

High-Performance Pseudo-Random Number Generation on Graphics Processing Units

Nimalan Nandapalan; Richard P. Brent; Lawrence M. Murray; Alistair P. Rendell;
Open Access
  • Published: 02 Aug 2011
  • Publisher: Springer Berlin Heidelberg
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.
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free text 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, Software deployment, Number theory, Parallel computing, Architecture, Monte Carlo method, CUDA, Graphics, Pseudo random number generation, Computer graphics, Computer science
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