We present MadFlow, a first general multi-purpose framework for Monte Carlo (MC) event simulation of particle physics processes designed to take full advantage of hardware accelerators, in particular, graphics processing units (GPUs). The automation process of generating all the required components for MC simulation of a generic physics process and its deployment on hardware accelerator is still a big challenge nowadays. In order to solve this challenge, we design a workflow and code library which provides to the user the possibility to simulate custom processes through the MadGraph5_aMC@NLO framework and a plugin for the generation and exporting of specialized code in a GPU-like format. The exported code includes analytic expressions for matrix elements and phase space. The simulation is performed using the VegasFlow and PDFFlow libraries which deploy automatically the full simulation on systems with different hardware acceleration capabilities, such as multi-threading CPU, single-GPU and multi-GPU setups. The package also provides an asynchronous unweighted events procedure to store simulation results. Crucially, although only Leading Order is automatized, the library provides all ingredients necessary to build full complex Monte Carlo simulators in a modern, extensible and maintainable way. We show simulation results at leading-order for multiple processes on different hardware configurations.

我们介绍MadFlow，第一个用于粒子物理过程的蒙特卡罗 (MC) 事件模拟的通用框架，旨在充分利用硬件加速器，特别是图形处理单元 (GPU)。生成通用物理过程的 MC 仿真所需的所有组件的自动化过程及其在硬件加速器上的部署，在当今仍然是一个巨大的挑战。为了解决这一挑战，我们设计了一个工作流和代码库，为用户提供通过 MadGraph5_aMC@NLO 框架模拟自定义流程的可能性，以及一个用于生成和导出类 GPU 格式的专用代码的插件。导出的代码包括矩阵元素和相空间的解析表达式。模拟是使用 VegasFlow 和 PDFFlow 库执行的，它们会在具有不同硬件加速功能的系统上自动部署完整的模拟，例如多线程 CPU、单 GPU 和多 GPU 设置。该包还提供了一个异步未加权事件程序来存储模拟结果。至关重要的是，虽然只有 Lead Order 是自动化的，但该库提供了以现代、可扩展和可维护的方式构建完整复杂的 Monte Carlo 模拟器所需的所有要素。我们显示了不同硬件配置上多个进程的领先顺序的模拟结果。该包还提供了一个异步未加权事件程序来存储模拟结果。至关重要的是，虽然只有 Lead Order 是自动化的，但该库提供了以现代、可扩展和可维护的方式构建完整复杂的 Monte Carlo 模拟器所需的所有要素。我们显示了不同硬件配置上多个进程的领先顺序的模拟结果。该包还提供了一个异步未加权事件程序来存储模拟结果。至关重要的是，虽然只有 Lead Order 是自动化的，但该库提供了以现代、可扩展和可维护的方式构建完整复杂的 Monte Carlo 模拟器所需的所有要素。我们显示了不同硬件配置上多个进程的领先顺序的模拟结果。