Hardware acceleration for next-to-leading order event generation within MadGraph5_aMC@NLO

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.

Authors

Institutions

Publication Details

Journal
EPJ Web of Conferences
Published
2025-01-01
DOI
https://doi.org/10.1051/epjconf/202533701230
Citations
1
Primary Topic
Particle physics theoretical and experimental studies
Type
article
Field-Weighted Citation Impact
1.87
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Hardware acceleration for next-to-leading order event generation within MadGraph5_aMC@NLO

Marco Zaro, A. Valassi, Zenny Wettersten, Olivier Mattelaer et al.
1 citations
EPJ Web of Conferences
Particle physics theoretical and experimental studies
1.87
article

Hardware acceleration for next-to-leading order event generation within MadGraph5_aMC@NLO

Marco Zaro, A. Valassi, Zenny Wettersten, Olivier Mattelaer, Stefan Roiser
article en
1 citations

Abstract

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.

EPJ Web of ConferencesVol. 337
University of Vienna (AT), TU Wien (AT), University of Milan (IT), European Organization for Nuclear Research (CH), UCLouvain (BE)
Openalex Percentile: Top 11%
Particle physics theoretical and experimental studies
1.87
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Hardware acceleration for next-to-leading order event generation within MadGraph5_aMC@NLO — Marco Zaro, A. Valassi, et al. · EPJ Web of Conferences (2025) | TGRS Research Map | TGRS