On The Performance of Prefix-Sum Parallel Kalman Filters and Smoothers on GPUs
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908650590699520 |
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| author | Särkkä, Simo García-Fernández, Ángel F. |
| author_facet | Särkkä, Simo García-Fernández, Ángel F. |
| contents | This paper presents an experimental evaluation of parallel-in-time Kalman filters and smoothers using graphics processing units (GPUs). In particular, the paper evaluates different all-prefix-sum algorithms, that is, parallel scan algorithms for temporal parallelization of Kalman filters and smoothers in two ways: by calculating the required number of operations via simulation, and by measuring the actual run time of the algorithms on real GPU hardware. In addition, a novel parallel-in-time two-filter smoother is proposed and experimentally evaluated. Julia code for Metal and CUDA implementations of all the algorithms is made publicly available. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_10363 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | On The Performance of Prefix-Sum Parallel Kalman Filters and Smoothers on GPUs Särkkä, Simo García-Fernández, Ángel F. Computation Distributed, Parallel, and Cluster Computing Dynamical Systems This paper presents an experimental evaluation of parallel-in-time Kalman filters and smoothers using graphics processing units (GPUs). In particular, the paper evaluates different all-prefix-sum algorithms, that is, parallel scan algorithms for temporal parallelization of Kalman filters and smoothers in two ways: by calculating the required number of operations via simulation, and by measuring the actual run time of the algorithms on real GPU hardware. In addition, a novel parallel-in-time two-filter smoother is proposed and experimentally evaluated. Julia code for Metal and CUDA implementations of all the algorithms is made publicly available. |
| title | On The Performance of Prefix-Sum Parallel Kalman Filters and Smoothers on GPUs |
| topic | Computation Distributed, Parallel, and Cluster Computing Dynamical Systems |
| url | https://arxiv.org/abs/2511.10363 |