The demand for computational resources is steadily increasing in experimental high energy physics as the current collider experiments continue to accumulate huge amounts of data and physicists indulge in more complex and ambitious analysis strategies. This is especially true in the fields of hadron spectroscopy and flavour physics where the analyses often depend on complex multidimensional unbinned maximum-likelihood fits, with several dozens of free parameters, with an aim to study the internal structure of hadrons. Graphics processing units (GPUs) represent one of the most sophisticated and versatile parallel computing architectures that are becoming popular toolkits for high energy physicists to meet their computational demands. GooFit is an upcoming open-source tool interfacing ROOT/RooFit to the CUDA platform on NVIDIA GPUs that acts as a bridge between the MINUIT minimization algorithm and a parallel processor, allowing probability density functions to be estimated on multiple cores simultaneously. In this article, a full-fledged amplitude analysis framework developed using GooFit is tested for its speed and reliability. The four-dimensional fitter framework, one of the firsts of its kind to be built on GooFit, is geared towards the search for exotic tetraquark states in the \(B^0 \rightarrow J/\psi K \pi\) decays and can also be seamlessly adapted for other similar analyses. The GooFit fitter, running on GPUs, shows a remarkable improvement in the computing speed compared to a ROOT/RooFit implementation of the same analysis running on multi-core CPU clusters. Furthermore, it shows sensitivity to components with small contributions to the overall fit. It has the potential to be a powerful tool for sensitive and computationally intensive physics analyses.

随着当前对撞机实验不断积累大量数据，物理学家沉迷于更加复杂和雄心勃勃的分析策略，对高能物理实验的计算资源需求也在稳步增长。这在强子光谱学和风味物理学领域尤其如此，在这种领域中，分析通常依赖于具有数十个自由参数的复杂多维未绑定最大似然拟合，目的是研究强子的内部结构。图形处理单元（GPU）代表了最复杂，用途最广泛的并行计算体系结构之一，这些体系结构正成为高能物理学家满足其计算需求的流行工具包。GooFit是即将到来的开放源代码工具，可将ROOT / RooFit与NVIDIA GPU上的CUDA平台连接，充当MINUIT最小化算法与并行处理器之间的桥梁，允许同时在多个内核上估计概率密度函数。在本文中，对使用GooFit开发的成熟的幅度分析框架进行了速度和可靠性测试。四维钳工框架是在GooFit上建立的同类首个此类框架之一，旨在在四肢寻找夸克状态。\（B ^ 0 \ rightarrow J / \ psi K \ pi \）衰减，并且也可以无缝地用于其他类似的分析。与在多核CPU集群上运行的相同分析的ROOT / RooFit实现相比，运行在GPU上的GooFit装配工显示出了显着的计算速度提高。此外，它显示出对零部件的敏感性，对整体配合的贡献很小。它有可能成为进行灵敏且需要大量计算的物理分析的强大工具。