Parallel-in-Time Kalman Smoothing Using Orthogonal Transformations
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866910861001490432 |
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| author | Gargir, Shahaf Toledo, Sivan |
| author_facet | Gargir, Shahaf Toledo, Sivan |
| contents | We present a numerically-stable parallel-in-time linear Kalman smoother. The smoother uses a novel highly-parallel QR factorization for a class of structured sparse matrices for state estimation, and an adaptation of the SelInv selective-inversion algorithm to evaluate the covariance matrices of estimated states. Our implementation of the new algorithm, using the Threading Building Blocks (TBB) library, scales well on both Intel and ARM multi-core servers, achieving speedups of up to 47x on 64 cores. The algorithm performs more arithmetic than sequential smoothers; consequently it is 1.8x to 2.5x slower on a single core. The new algorithm is faster and scales better than the parallel Kalman smoother proposed by Särkkä and Garc\'ıa-Fernández in 2021. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_11686 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Parallel-in-Time Kalman Smoothing Using Orthogonal Transformations Gargir, Shahaf Toledo, Sivan Distributed, Parallel, and Cluster Computing Signal Processing We present a numerically-stable parallel-in-time linear Kalman smoother. The smoother uses a novel highly-parallel QR factorization for a class of structured sparse matrices for state estimation, and an adaptation of the SelInv selective-inversion algorithm to evaluate the covariance matrices of estimated states. Our implementation of the new algorithm, using the Threading Building Blocks (TBB) library, scales well on both Intel and ARM multi-core servers, achieving speedups of up to 47x on 64 cores. The algorithm performs more arithmetic than sequential smoothers; consequently it is 1.8x to 2.5x slower on a single core. The new algorithm is faster and scales better than the parallel Kalman smoother proposed by Särkkä and Garc\'ıa-Fernández in 2021. |
| title | Parallel-in-Time Kalman Smoothing Using Orthogonal Transformations |
| topic | Distributed, Parallel, and Cluster Computing Signal Processing |
| url | https://arxiv.org/abs/2502.11686 |