MoreFit.

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Bibliographic Details
Title: MoreFit.
Authors: Langenbruch, Christoph1 (AUTHOR) christoph.langenbruch@cern.ch
Source: European Physical Journal C -- Particles & Fields. Feb2026, Vol. 86 Issue 2, p1-15. 15p.
Subjects: Maximum likelihood statistics, Parallel programming, Mathematical optimization, Benchmark problems (Computer science), Particle physics, Computing platforms, Parameter estimation
Abstract: 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. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
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