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Evaluation of Pseudo-Random Number Generation on GPU Cards

Tair Askar, Bekdaulet Shukirgaliyev, Martin Lukac, Ernazar Abdikamalov
School of Engineering and Digital Sciences, Nazarbayev University, Nur-Sultan 010000, Kazakhstan
Computation, Volume 9, Issue 12, 2021

@article{askar2021evaluation,

   title={Evaluation of Pseudo-Random Number Generation on GPU Cards},

   author={Askar, Tair and Shukirgaliyev, Bekdaulet and Lukac, Martin and Abdikamalov, Ernazar},

   journal={Computation},

   volume={9},

   number={12},

   pages={142},

   year={2021},

   publisher={Multidisciplinary Digital Publishing Institute}

}

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Monte Carlo methods rely on sequences of random numbers to obtain solutions to many problems in science and engineering. In this work, we evaluate the performance of different pseudorandom number generators (PRNGs) of the Curand library on a number of modern Nvidia GPU cards. As a numerical test, we generate pseudo-random number (PRN) sequences and obtain non-uniform distributions using the acceptance-rejection method. We consider GPU, CPU, and hybrid CPU/GPU implementations. For the GPU, we additionally consider two different implementations using the host and device application programming interfaces (API). We study how the performance depends on implementation parameters, including the number of threads per block and the number of blocks per streaming multiprocessor. To achieve the fastest performance, one has to minimize the time consumed by PRNG seed setup and state update. The duration of seed setup time increases with the number of threads, while PRNG state update decreases. Hence, the fastest performance is achieved by the optimal balance of these opposing effects.
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