An O(log2N) Fully-Balanced Resampling Algorithm for Particle Filters on Distributed Memory Architectures

Varsi, Alessandro ORCID: 0000-0003-2218-4720, Maskell, Simon ORCID: 0000-0003-1917-2913 and Spirakis, Paul G ORCID: 0000-0001-5396-3749 (2021) An O(log2N) Fully-Balanced Resampling Algorithm for Particle Filters on Distributed Memory Architectures. Algorithms, 14 (12). p. 342.

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Abstract

<jats:p>Resampling is a well-known statistical algorithm that is commonly applied in the context of Particle Filters (PFs) in order to perform state estimation for non-linear non-Gaussian dynamic models. As the models become more complex and accurate, the run-time of PF applications becomes increasingly slow. Parallel computing can help to address this. However, resampling (and, hence, PFs as well) necessarily involves a bottleneck, the redistribution step, which is notoriously challenging to parallelize if using textbook parallel computing techniques. A state-of-the-art redistribution takes O((log2N)2) computations on Distributed Memory (DM) architectures, which most supercomputers adopt, whereas redistribution can be performed in O(log2N) on Shared Memory (SM) architectures, such as GPU or mainstream CPUs. In this paper, we propose a novel parallel redistribution for DM that achieves an O(log2N) time complexity. We also present empirical results that indicate that our novel approach outperforms the O((log2N)2) approach.</jats:p>

Item Type:	Article
Uncontrolled Keywords:	parallel computing, resampling, Particle Filters, high performance computing, Distributed Memory, message passing interface
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	30 Nov 2021 08:45
Last Modified:	15 Mar 2024 08:52
DOI:	10.3390/a14120342
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3144238