Comparing the performance of stochastic simulation on GPUs and OpenMP. (1st January 2013)

• Record Type: Journal Article
• Authors: Xiao, Weijun; Li, Peng; Lilja, David J.
• Is Part Of: International journal of computational science and engineering. Volume 8:Number 1(2013)
• Page Start: 34
• Page End: 46
• Publication Date: 2013-01-01
• Subjects: parallel computing -- GPU computing -- stochastic computing -- image processing -- fault-tolerance; Computer science -- Mathematics -- Periodicals; Computer simulation -- Mathematical aspects -- Periodicals; Computational intelligence -- Periodicals; 004.015105
• Languages: English
• ISSNs: 1742-7185

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.