High-performance pseudo-random number generation on graphics processing units
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...[Show more]
|Collections||ANU Research Publications|
|Source:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|01_Nandapalan_High-performance_pseudo-random_2012.pdf||168.97 kB||Adobe PDF||Request a copy|
|02_Nandapalan_High-performance_pseudo-random_2012.pdf||126.48 kB||Adobe PDF||Request a copy|
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