Accelerating detector simulations with Celeritas: Profiling and performance optimizations

Celeritas is a GPU-optimized Monte Carlo (MC) particle transport code designed to meet the growing computational demands of next-generation high energy physics (HEP) experiments. It provides efficient simulation of electromagnetic (EM) physics processes in complex geometries with magnetic fields, detector hit scoring, and seamless integration into Geant4-driven applications to offload EM physics to GPUs. Recent efforts have focused on performance optimizations and expanding profiling capabilities. This paper presents some key advancements, including the integration of the Perfetto system profiling tool for detailed performance analysis and the development of track-sorting methods to improve computational efficiency.

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Publication Details

Journal
EPJ Web of Conferences
Published
2025-01-01
DOI
https://doi.org/10.1051/epjconf/202533701292
Citations
1
Primary Topic
Particle physics theoretical and experimental studies
Type
article
Field-Weighted Citation Impact
1.87

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Accelerating detector simulations with Celeritas: Profiling and performance optimizations

Elliott Biondo, S. R. Johnson, S. Y. Jun, S. C. Tognini et al.
1 citations
EPJ Web of Conferences
Particle physics theoretical and experimental studies
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article

Accelerating detector simulations with Celeritas: Profiling and performance optimizations

Elliott Biondo, S. R. Johnson, S. Y. Jun, S. C. Tognini, Thomas Evans, Amanda Lund, Benjamin J. Morgan, Julien Esseiva, J. G. R. Lima, Philippe Canal, Hayden Hollenbeck
article en
1 citations

Abstract

Celeritas is a GPU-optimized Monte Carlo (MC) particle transport code designed to meet the growing computational demands of next-generation high energy physics (HEP) experiments. It provides efficient simulation of electromagnetic (EM) physics processes in complex geometries with magnetic fields, detector hit scoring, and seamless integration into Geant4-driven applications to offload EM physics to GPUs. Recent efforts have focused on performance optimizations and expanding profiling capabilities. This paper presents some key advancements, including the integration of the Perfetto system profiling tool for detailed performance analysis and the development of track-sorting methods to improve computational efficiency.

EPJ Web of ConferencesVol. 337
Oak Ridge Associated Universities (US), Argonne National Laboratory (US), Oak Ridge National Laboratory (US), Fermi National Accelerator Laboratory (US), Lawrence Berkeley National Laboratory (US), University of Warwick (GB), Urology of Virginia (US), University of Virginia (US)
U.S. Department of Energy, Battelle, UT-Battelle, National Energy Research Scientific Computing Center, China Scholarship Council, Office of Science, Advanced Scientific Computing Research, High Energy Physics, Oak Ridge National Laboratory, Fermilab, Lawrence Berkeley National Laboratory
Openalex Percentile: Top 11%
Particle physics theoretical and experimental studies
1.87
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