| Issue |
EPJ Web Conf.
Volume 337, 2025
27th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2024)
|
|
|---|---|---|
| Article Number | 01230 | |
| Number of page(s) | 8 | |
| DOI | https://doi.org/10.1051/epjconf/202533701230 | |
| Published online | 07 October 2025 | |
https://doi.org/10.1051/epjconf/202533701230
Hardware acceleration for next-to-leading order event generation within MadGraph5_aMC@NLO
1 European Organization for Nuclear Research (CERN)
2 Technical University of Vienna (TU Wien)
3 University of Louvain (UCLouvain)
4 University of Milan (UniMi)
* e-mail: zenny.wettersten@cern.ch
Published online: 7 October 2025
As the quality of experimental measurements increases, so does the need for Monte Carlo-generated simulated events — both with respect to the total amount and to their precision. In perturbative methods, this involves the evaluation of higher order corrections to the leading order (LO) scattering amplitudes, including real emissions and loop corrections. Although experimental uncertainties today are larger than those of simulations, at the High Luminosity LHC experimental uncertainties are expected to be smaller than the theoretical uncertainty for events generated below next-to-leading order (NLO) precision. As forecasted hardware resources will not meet CPU requirements for these simulation needs, speeding up NLO event generation is a necessity.
In recent years, collaborators across Europe and the United States have been working on CPU vectorisation of LO event generation within the Mad- Graph5_aMC@NLO framework, as well as porting it to GPUs, to major success. Recently, development has also started on vectorising NLO event generation. Due to the more complicated nature of NLO amplitudes this development faces several difficulties not accounted for in the LO development, but it shows promise. Here, we present these issues as well as the current status of our eventparallel NLO implementation.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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