jaxFMM: An Adaptive, GPU-Parallel Implementation of the Fast Multipole Method in JAX
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914164265451520 |
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| author | Kraft, Robert Bruckner, Florian Suess, Dieter Abert, Claas |
| author_facet | Kraft, Robert Bruckner, Florian Suess, Dieter Abert, Claas |
| contents | We introduce jaxFMM, an open-source, adaptive, highly parallel point-charge Fast Multipole Method implementation for the Laplace kernel written in JAX. It is based on a non-uniform refinement strategy, which results in extremely concise and simple code. Benchmarks show that the algorithm performs well even for highly non-uniform charge distributions. JaxFMM already massively speeds up stray-field computations in micromagnetics and with JAX features like autodiff, novel applications such as inverse-design problems and machine-learning tasks can be tackled with ease in the future. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_15269 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | jaxFMM: An Adaptive, GPU-Parallel Implementation of the Fast Multipole Method in JAX Kraft, Robert Bruckner, Florian Suess, Dieter Abert, Claas Computational Physics 65R20 We introduce jaxFMM, an open-source, adaptive, highly parallel point-charge Fast Multipole Method implementation for the Laplace kernel written in JAX. It is based on a non-uniform refinement strategy, which results in extremely concise and simple code. Benchmarks show that the algorithm performs well even for highly non-uniform charge distributions. JaxFMM already massively speeds up stray-field computations in micromagnetics and with JAX features like autodiff, novel applications such as inverse-design problems and machine-learning tasks can be tackled with ease in the future. |
| title | jaxFMM: An Adaptive, GPU-Parallel Implementation of the Fast Multipole Method in JAX |
| topic | Computational Physics 65R20 |
| url | https://arxiv.org/abs/2511.15269 |