Energy Efficiency and Performance Analysis of Monte Carlo Event Generators for High Energy Physics

The objective of this project is to study the performance and energy consumption of Monte Carlo event generators used in High-Energy Physics (HEP) simulations with the Next Generation Triggers project. The project builds upon ongoing efforts to optimize event generators such as MadGraph5_aMC@NLO, evaluating their performance across various computational backends—including GPU acceleration with both NVIDIA and AMD GPUs, and CPU-based acceleration using vector instructions on Intel and ARM processors. The work involves the use of several profiling and monitoring tools to gain insights into hardware utilization, memory efficiency, and power consumption. This research has broader implications for the HEP community, addressing the urgent need for sustainable computational practices, improved development and profiling toolchains, and optimized computing infrastructure. It also offers the opportunity to contribute to cutting-edge research at the CERN IT department, collaborating with a team of experienced researchers.

Publication Details

Published
2025-08-14
DOI
https://doi.org/10.17181/0fxf4-p0k59
Primary Topic
Distributed and Parallel Computing Systems
Type
article
Field-Weighted Citation Impact
0.00
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article

Energy Efficiency and Performance Analysis of Monte Carlo Event Generators for High Energy Physics

Distributed and Parallel Computing Systems
article

Energy Efficiency and Performance Analysis of Monte Carlo Event Generators for High Energy Physics

article en

Abstract

The objective of this project is to study the performance and energy consumption of Monte Carlo event generators used in High-Energy Physics (HEP) simulations with the Next Generation Triggers project. The project builds upon ongoing efforts to optimize event generators such as MadGraph5_aMC@NLO, evaluating their performance across various computational backends—including GPU acceleration with both NVIDIA and AMD GPUs, and CPU-based acceleration using vector instructions on Intel and ARM processors. The work involves the use of several profiling and monitoring tools to gain insights into hardware utilization, memory efficiency, and power consumption. This research has broader implications for the HEP community, addressing the urgent need for sustainable computational practices, improved development and profiling toolchains, and optimized computing infrastructure. It also offers the opportunity to contribute to cutting-edge research at the CERN IT department, collaborating with a team of experienced researchers.

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