MadFlow: towards the automation of Monte Carlo simulation on GPU for particle physics processes
MadFlow: towards the automation of Monte Carlo simulation on GPU for particle physics processes
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MadFlow：在 GPU 上实现粒子物理过程蒙特卡罗模拟的自动化
作者：
S. Carrazza;J. Cruz;Marco Rossi;M. Zaro
在本程序中，我们提出了MadFlow，一个新的框架，用于粒子物理过程的图形处理单元（GPU）上的Monte Carlo（MC）模拟自动化。为了自动化MC模拟的通用数量的过程中，我们设计了一个程序，它提供给用户的可能性，通过Mad-Graph 5_aMC@NLO框架来模拟自定义过程。该流水线包括第一阶段，其中生成矩阵元素和相空间的解析表达式并以类似GPU的格式导出。然后使用VegasFlow和PDFFlow库执行模拟，这些库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。我们展示了一些初步的结果，在不同的硬件配置的领导阶模拟。
In this proceedings we present MadFlow, a new framework for the automation of Monte Carlo (MC) simulation on graphics processing units (GPU) for particle physics processes. In order to automate MC simulation for a generic number of processes, we design a program which provides to the user the possibility to simulate custom processes through the Mad-Graph5_aMC@NLO framework. The pipeline includes a first stage where the analytic expressions for matrix elements and phase space are generated and exported in a GPU-like format. The simulation is then 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. We show some preliminary results for leading-order simulations on different hardware configurations.