MoreFit: A More Optimised, Rapid and Efficient Fit
Fuente:
arXiv
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866917247301189632 |
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| author | Langenbruch, Christoph |
| author_facet | Langenbruch, Christoph |
| contents | Parameter estimation via unbinned maximum likelihood fits is a central technique in particle physics. This article introduces MoreFit, which aims to provide a more optimised, rapid and efficient fitting solution for unbinned maximum likelihood fits. MoreFit is developed with a focus on parallelism and relies on computation graphs that are compiled just-in-time. Several novel automatic optimisation techniques are employed on the computation graphs that significantly increase performance compared to conventional approaches. MoreFit can make efficient use of a wide range of heterogeneous platforms through its compute backends that rely on open standards. It provides an OpenCL backend for execution on GPUs of all major vendors, and a backend based on LLVM and Clang for single- or multithreaded execution on CPUs, which in addition allows for SIMD vectorisation. MoreFit is benchmarked against several other fitting frameworks and shows very promising performance, illustrating the power of the approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_12414 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MoreFit: A More Optimised, Rapid and Efficient Fit Langenbruch, Christoph Data Analysis, Statistics and Probability High Energy Physics - Experiment Parameter estimation via unbinned maximum likelihood fits is a central technique in particle physics. This article introduces MoreFit, which aims to provide a more optimised, rapid and efficient fitting solution for unbinned maximum likelihood fits. MoreFit is developed with a focus on parallelism and relies on computation graphs that are compiled just-in-time. Several novel automatic optimisation techniques are employed on the computation graphs that significantly increase performance compared to conventional approaches. MoreFit can make efficient use of a wide range of heterogeneous platforms through its compute backends that rely on open standards. It provides an OpenCL backend for execution on GPUs of all major vendors, and a backend based on LLVM and Clang for single- or multithreaded execution on CPUs, which in addition allows for SIMD vectorisation. MoreFit is benchmarked against several other fitting frameworks and shows very promising performance, illustrating the power of the approach. |
| title | MoreFit: A More Optimised, Rapid and Efficient Fit |
| topic | Data Analysis, Statistics and Probability High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2505.12414 |