Comparing the performance of stochastic simulation on GPUs and OpenMP

Authors:
Weijun Xiao;Peng Li;David J. Lilja
Affiliations:
Department of Electrical and Computer Engineering University of Minnesota, Twin Cities, Minneapolis, MN, 55455, USA;Department of Electrical and Computer Engineering University of Minnesota, Twin Cities, Minneapolis, MN, 55455, USA;Department of Electrical and Computer Engineering University of Minnesota, Twin Cities, Minneapolis, MN, 55455, USA
Venue:
International Journal of Computational Science and Engineering
Year:
2013

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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.