NDLI: An $O(log_{2}$N) Fully-Balanced Resampling Algorithm for Particle Filters on Distributed Memory Architectures

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An $O(log_{2}$N) Fully-Balanced Resampling Algorithm for Particle Filters on Distributed Memory Architectures

Content Provider	MDPI
Author	Varsi, Alessandro Maskell, Simon Spirakis, Paul G.
Copyright Year	2021
Abstract	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 $O ({({log}_{2} N)}^{2})$ computations on Distributed Memory (DM) architectures, which most supercomputers adopt, whereas redistribution can be performed in $O ({log}_{2} N)$ 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 $O ({log}_{2} N)$ time complexity. We also present empirical results that indicate that our novel approach outperforms the $O ({({log}_{2} N)}^{2})$ approach.
Starting Page	342
e-ISSN	19994893
DOI	10.3390/a14120342
Journal	Algorithms
Issue Number	12
Volume Number	14
Language	English
Publisher	MDPI
Publisher Date	2021-11-26
Access Restriction	Open
Subject Keyword	Algorithms Hardware and Architecturee Parallel Computing Resampling Particle Filters High Performance Computing Distributed Memory Message Passing Interface
Content Type	Text
Resource Type	Article

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