MadFlow: automating Monte Carlo simulation on GPU for particle physics processes

TL;DR

MadFlow is a versatile GPU-accelerated framework that automates Monte Carlo event simulations in particle physics, enabling efficient, customizable, and scalable computations across various hardware setups.

Contribution

It introduces a general, automated workflow for GPU-based Monte Carlo simulations in particle physics, integrating existing tools with new code generation and deployment capabilities.

Findings

Successful simulation of multiple processes on different hardware configurations.

Demonstrated scalability from CPU to multi-GPU systems.

Automated generation of process-specific code for efficient GPU execution.

Abstract

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.