Watts per event evaluating Sustainability of HEP Event Generators beyond the LHC era

Jul 16, 2026

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Monte Carlo event generators for high-energy physics require major computing resources for development, tuning, and production. This study evaluates the sustainability of such tools using a containerized workflow (77rev/propripy) and benchmarks the HIJING++ heavy-ion Monte Carlo event generator. Performance is analyzed across CPU architectures, showing that appropriate multithreading selection can optimize per-event generation cost. The framework targets reduced energy footprints and supports carbon-aware benchmarking for next-collider simulation campaigns.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages20

Reading Time50 min

Introduction

Efficiency Scoring

Motivation: Monte Carlo tuning

Methodology

arXiv :2607 .05018v1 [physics .comp-ph] 6 Jul 2026 Watts per event: evaluating Sustainability of HEP Event Generators beyond the LHC era Szabolcs Moln´ar 1 , G´abor B´ır´o 1 , G´abor Papp2 , Gergely G´abor Barnaf¨oldi 1 1 Department of Theoretical Physics, HUN-REN Wigner Research Centre for Physics, 29-33 Konkoly-Thege Mikl´os rd., Budapest, 1121, Hungary. 2 Department of Theoretical Physics, E¨otv¨os Lor´and University, P´azm´any P´eter S´et´any 1/A, Budapest, H-1117, Hungary. Contributing authors: molnar.szabolcs@wigner.hun-ren.hu; biro.gabor@wigner.hun-ren.hu; [gabor.papp@ttk.elte.hu](gabor.papp@ttk.elte.hu) ; barnafoldi.gergely@wigner.hun-ren.hu; Abstract The development, tuning and operation of Monte Carlo event generators beyond the LHC era require vast amount of resources. In this study we investigate the sustainability of these software with a containerized set of tools (named 77rev/propripy), by benchmarking the HIJING++ heavy-ion Monte Carlo event generator. We analyze the performance of various CPU architectures and show that by choosing the level of multithreading properly, the cost of event generation can be optimized. The presented approach can reduce the energy footprint of high-energy physics event generators and therefore alleviate the ever-increasing, ubiquitous computational challenges. 1 Introduction The upcoming High-Luminosity LHC (HL-LHC), and later the Future Circular Collider (FCC) era present unprecedented computational challenges, as the required scale of simulated data is expected to increase by an order of magnitude [1–4] . Monte Carlo (MC) event generation remains one of the most resource-intensive components of the HEP computing pipeline, often consuming a majority of the total CPU power provided by the Worldwide LHC Computing Grid (WLCG) [5] . As the community shifts 1 toward a ”carbon-aware” computing model, the metric for success is evolving from simple event throughput to sustainable efficiency [6] . This becomes especially important if the globally increasing hardware prices are showing an increasing trend. Monte Carlo event generators are computational tools that simulate the complete evolution of particle collisions. The calculations are separated into two parts: hard and soft processes. Quantum Chromodynamics (QCD) is a non-abelian gauge theory that describes the strong interaction [7] . On high energies QCD is perturbative, while on low energies where the coupling becomes strong it is non-perturbative. This nonperturbative energy domain is what we refer to as soft QCD and is handled by effective theories or phenomenology. Overall, these properties make it challenging to model particle collisions accurately. The non-perturbative nature of the soft processes and the requirement to model both hard and soft parts necessitate many parameters, some of which are non-physical. For the model to have predictive power, all of the parameters have to be tuned [8, 9] using experimental data, e.g. from the LHC [10] . In the following sections, we introduce a scoring system to measure efficiency, anda specifically developed toolbox for its evaluation. We illustrate the methodology by presenting the results measured via a heavy-ion Monte Carlo event generator at LHC and FCC energies. 2 Efficiency Scoring Current HEP computing demands a departure from traditional ”time-to-completion”metrics. As software grows more complex—incorporating deep multithreading and vectorization—the hardware utilization profile changes significantly. Modern workloads require a benchmarking approach that prioritizes real-world application performance over synthetic instruction sets. By focusing on the operational efficiency of specific production tasks, researchers can better understand how hardware limitations, such as memory bandwidth and thermal throttling, impact the overall throughput of large-scale simulation campaigns. To evaluate the sustainability of an event generator, a ”price” metr

FAQs

How does the study define the energy cost per physics event?

It introduces a “price” metric by correlating average consumed power with event throughput, using E_event = P_avg / T_event in the paper’s formulation to obtain per-event energy cost.

Why is multithreading selection important for sustainable event generation?

The study shows that different CPU architectures respond differently, and that choosing an appropriate multithreading level can optimize the event-generation cost under real benchmarking assumptions.

What tools and workflow are used to support Monte Carlo tuning?

The tuning relies on iterative minimization using the Professor tool, with event generation producing HepMC3 files, analysis performed via Rivet, intermediate results stored in YODA format, and then interpolation and cost-function minimization in Professor.

Jul 16, 2026

Upload by Noah