Developments in Performance and Portability for MadGraph5_aMC@NLO

TL;DR

This paper discusses recent advancements in optimizing the MadGraph5_aMC@NLO event generator for heterogeneous computing architectures, including CPUs and GPUs, to enhance performance and portability for high energy physics simulations.

Contribution

The work introduces new code generation techniques for matrix element calculations using C++, CUDA, Alpaka, Kokkos, and SYCL, enabling efficient execution across diverse hardware architectures.

Findings

Performance improvements in matrix element calculations on various hardware.

Successful porting of the code to GPU and CPU architectures using multiple frameworks.

Progress towards integrating these advancements into a production release for LHC experiments.

Abstract

Event generators simulate particle interactions using Monte Carlo techniques, providing the primary connection between experiment and theory in experimental high energy physics. These software packages, which are the first step in the simulation worflow of collider experiments, represent approximately 5 to 20% of the annual WLCG usage for the ATLAS and CMS experiments. With computing architectures becoming more heterogeneous, it is important to ensure that these key software frameworks can be run on future systems, large and small. In this contribution, recent progress on porting and speeding up the Madgraph5_aMC@NLO event generator on hybrid architectures, i.e. CPU with GPU accelerators, is discussed. The main focus of this work has been in the calculation of scattering amplitudes and "matrix elements", which is the computational bottleneck of an event generation application. For physics processes limited to QCD leading order, the code generation toolkit has been expanded to produce matrix element calculations using C++ vector instructions on CPUs and using CUDA for NVidia GPUs, as well as using Alpaka, Kokkos and SYCL for multiple CPU and GPU architectures. Performance is reported in terms of matrix element calculations per time on NVidia, Intel, and AMD devices. The status and outlook for the integration of this work into a production release usable by the LHC experiments, with the same functionalities and very similar user interfaces as the current Fortran version, is also described.