Title: Acceleration of sequential Monte Carlo for computationally intensive target distribution by parallel lookup table and GPU computing

Authors: Di Zhao

Addresses: Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100190, China

Abstract: Sequential Monte Carlo (SMC) is the key solver for applications such as object tracking, signal processing and statistical distribution approximation. However, if the target distribution is complicated, the solution speed of the conventional SMC is too slow to satisfy the real-time requirement of applications. In this paper, by the novel idea of GPU-based lookup table (GPULTU), the acceleration method for the conventional SMC (LTU-GPU accelerated SMC) is developed, and the efficiency of LTU-GPU accelerated SMC by a statistical approximation problem is illustrated. Computational results show that LTU-GPU accelerated SMC is significantly faster than the conventional SMC from hours to seconds.

Keywords: sequential Monte Carlo; SMC; parallel lookup table; GPU computing; statistical distribution approximation.

DOI: 10.1504/IJHPCN.2018.093841

International Journal of High Performance Computing and Networking, 2018 Vol.12 No.1, pp.49 - 55

Received: 12 Nov 2015
Accepted: 11 Jun 2016

Published online: 27 Jul 2018 *

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