Publication
IEEE SPL
Paper

A fast parallel particle filter for shared memory systems

View publication

Abstract

Particle Filters (PFs) are Sequential Monte Carlo methods which are widely used to solve filtering problems of dynamic models under Non-Linear Non-Gaussian noise. Modern PF applications have demanding accuracy and run-time constraints that can be addressed through parallel computing. However, an efficient parallelization of PFs can only be achieved by effectively parallelizing the bottleneck: Resampling and its constituent redistribution step. A pre-existing implementation of redistribute on Shared Memory Architectures (SMAs) achieves O(N T log2N) time complexity over T parallel cores. This redistribute implementation is, however, highly computationally intensive and cannot be effectively parallelized due to the inherently limited number of cores of SMAs. In this paper, we propose a novel parallel redistribute on OpenMP 4.5 which takes O(N T + log2N) steps and fully exploits the computational power of SMAs. The proposed approach is up to six times faster than the O(N T log2N) one and its implementation on GPU provides a further three-time speed-up vs its equivalent on a 32-core CPU.We also show on an exemplary PF that our redistribution is no longer the bottleneck.

Date

01 Jan 2020

Publication

IEEE SPL

Authors

Share