Comparing the performance of stochastic simulation on GPUs and OpenMP
by Weijun Xiao; Peng Li; David J. Lilja
International Journal of Computational Science and Engineering (IJCSE), Vol. 8, No. 1, 2013

Abstract: Since stochastic computing performs operations using streams of bits that represent probability values instead of deterministic values, it can tolerate a large number of failures in a noisy system. However, the simulation of a stochastic implementation is extremely time-consuming. In this paper, we investigate two approaches to speed up the stochastic simulation: a GPU-based simulation and an OpenMP-based simulation. To compare these two approaches, we start with several basic stochastic computing elements (SCEs) and then use the stochastic implementation of a frame difference-based image segmentation algorithm as case study to conduct extensive experiments. Measured results show that the GPU-based simulation with 448 processing elements can achieve up to 119x performance speedup compared to the single-threaded CPU simulation and 17x performance speedup over the OpenMP-based simulation with eight processor cores. In addition, we present several performance optimisations for the GPU-based simulation which significantly benefit the performance of stochastic simulation.

Online publication date: Fri, 27-Dec-2013

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