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A Fast Parallel Particle Filter for Shared Memory Systems

delete2020-01-01
delete15
PRE
AI
A
Alessandro Varsi *
J
Jack Taylor
L
Lykourgos Kekempanos
E
Edward Knapp
S
Simon Maskell
DOI:10.1109/LSP.2020.3014035delete
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摘要

摘要

En 中文
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 log(2)N) 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 + log(2)N) steps and fully exploits the computational power of SMAs. The proposed approach is up to six times faster than the O(N/T log(2)N) 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.
Keyword:
Parallel particle filters
shared memory architectures
OpenMP
resampling
redistribute
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期刊

IEEE Signal Processing Magazine 封面图
IEEE Signal Processing Magazine
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1.1W
被引数:
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STFC Daresbury Laboratory
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