MoreFit: A More Optimised, Rapid and Efficient Fit

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.

28 Pages, 7 figures, provided additional details on timing, matches published version

References in corpus (6)

• A Unified Approach to the Classical Statistical Analysis of Small Signals
• Symmetries and Asymmetries of B -> K* mu+ mu- Decays in the Standard Model and Beyond
• Angular analysis of the $B^0\to K^{\ast 0}{\mu }^{+}{\mu }^{-}$ decay using $3\,\mbox{fb}^{-1}$ of integrated luminosity
• Measurement of $CP$-averaged observables in the $B^0\to K^{\ast 0}{\mu }^{+}{\mu }^{-}$ decay
• Parameter uncertainties in weighted unbinned maximum likelihood fits
• GooFit: A library for massively parallelising maximum-likelihood fits