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Reliable Initialization of GPU-enabled Parallel Stochastic Simulations Using Mersenne Twister for Graphics Processors

Jonathan Passerat-Palmbach, Claude Mazel, Antoine Mahul, David Hill
ISIMA, Institut Superieur d’Informatique, de Modelisation et de ses Appplications, BP 10125, F-63173, AUBIERE
arXiv:1501.07701 [cs.DC], (30 Jan 2015)

@article{passerat-palmbach2015reliable,

   title={Reliable Initialization of GPU-enabled Parallel Stochastic Simulations Using Mersenne Twister for Graphics Processors},

   author={Passerat-Palmbach, Jonathan},

   year={2015},

   month={jan},

   archivePrefix={"arXiv"},

   primaryClass={cs.DC}

}

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Parallel stochastic simulations tend to exploit more and more computing power and they are now also developed for General Purpose Graphics Process Units (GP-GPUs). Consequently, they need reliable random sources to feed their applications. We propose a survey of the current Pseudo Random Numbers Generators (PRNG) available on GPU. We give a particular focus to the recent Mersenne Twister for Graphics Processors (MTGP) that has just been released. Our work provides empirically checked statuses designed to initialize a particular configuration of this generator, in order to prevent any potential bias introduced by the parallelization of the PRNG.
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