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 GPU-based PRNGs, demonstrating favourable performance of the xorgens-based approach.
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Nandapalan, N., Brent, R.P., Murray, L.M., Rendell, A.P. (2012). High-Performance Pseudo-Random Number Generation on Graphics Processing Units. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Waśniewski, J. (eds) Parallel Processing and Applied Mathematics. PPAM 2011. Lecture Notes in Computer Science, vol 7203. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31464-3_62
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DOI: https://doi.org/10.1007/978-3-642-31464-3_62
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