Article:
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Ch. Riesinger, T. Neckel, F. Rupp, A. Parra Hinojosa & H.-J. Bungartz:
GPU Optimization of Pseudo Random Number Generators for Random Ordinary Differential
Equations

In Proceedings of the International Conference on Computational Science (ICCS'14),
Volume 29, p. 172–183, June 2014. 


Abstract:
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Solving differential equations with stochastic terms involves a massive use of pseudo
random numbers. We present an application for the simulation of wireframe buildings
under stochastic earthquake excitation. The inherent potential for vectorization of
the application is used to its full extent on GPU accelerator hardware. A representative
set of pseudo random number gen- erators for uniformly and normally distributed pseudo
random numbers has been implemented, optimized, and benchmarked. The resulting optimized
variants outperform standard library implementations on GPUs. The techniques and
improvements shown in this contribution using the Kanai-Tajimi model can be generalized
to other random differential equations or stochastic models as well as other accelerators.