标题： 更佳拟合（MoreFit）：一种更为优化、快速且高效的拟合方法 显示英文标题

标题： MoreFit: A More Optimised, Rapid and Efficient Fit

摘要： 通过非分箱最大似然拟合进行参数估计是粒子物理学中的核心方法。 本文介绍了MoreFit，它旨在为非分箱最大似然拟合提供一种更优化、快速且高效的拟合解决方案。 MoreFit的开发重点在于并行性，并依赖即时编译的计算图。 在计算图上采用了多种新颖的自动优化技术，与传统方法相比显著提高了性能。 MoreFit通过依赖开放标准的计算后端，能够高效利用各种异构平台。 它提供了OpenCL后端以在所有主要厂商的GPU上执行，以及基于LLVM和Clang的后端，用于CPU上的单线程或多线程执行，此外还支持SIMD向量化。 MoreFit与多个其他拟合框架进行了基准测试，展示了非常有前景的性能，体现了该方法的强大之处。 显示英文摘要

摘要： 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.