Pseudo-Random Streams for Distributed and Parallel Stochastic Simulations on GP-GPUReport as inadecuate

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1 ISIMA - Institut Supérieur d-Informatique, de Modélisation et de leurs Applications 2 UBP - Université Blaise Pascal - Clermont-Ferrand 2 3 LIMOS - Laboratoire d-Informatique, de Modélisation et d-optimisation des Systèmes 4 Clermont Université

Abstract : Random number generation is a key element of stochastic simulations. It has been widely studied for sequential applications purposes, enabling us to reliably use pseudo-random numbers in this case. Unfortunately, we cannot be so enthusiastic when dealing with parallel stochastic simulations. Many applications still neglect random stream parallelization, leading to potentially biased results. In particular parallel execution platforms, such as Graphics Processing Units GPUs, add their constraints to those of Pseudo-Random Number Generators PRNGs used in parallel. This results in a situation where potential biases can be combined with performance drops when parallelization of random streams has not been carried out rigorously. Here, we propose criteria guiding the design of good GPU-enabled PRNGs. We enhance our comments with a study of the techniques aiming to parallelize random streams correctly, in the context of GPU-enabled stochastic simulations.

Keywords : Stochastic Simulations GP-GPU Pseudo-Random Number Generators Random Stream Parallelization

Author: Jonathan Passerat-Palmbach - Claude Mazel - David Hill -



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