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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.17608 |
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| _version_ | 1866909547845648384 |
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| author | Lund, Amanda L. Esseiva, Julien Johnson, Seth R. Biondo, Elliott Canal, Philippe Evans, Thomas Hollenbeck, Hayden Jun, Soon Yung Lima, Guilherme Morgan, Ben Tognini, Stefano C. |
| author_facet | Lund, Amanda L. Esseiva, Julien Johnson, Seth R. Biondo, Elliott Canal, Philippe Evans, Thomas Hollenbeck, Hayden Jun, Soon Yung Lima, Guilherme Morgan, Ben Tognini, Stefano C. |
| contents | Celeritas is a GPU-optimized MC particle transport code designed to meet the growing computational demands of next-generation HEP experiments. It provides efficient simulation of 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_17608 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Accelerating detector simulations with Celeritas: profiling and performance optimizations Lund, Amanda L. Esseiva, Julien Johnson, Seth R. Biondo, Elliott Canal, Philippe Evans, Thomas Hollenbeck, Hayden Jun, Soon Yung Lima, Guilherme Morgan, Ben Tognini, Stefano C. Computational Physics Celeritas is a GPU-optimized MC particle transport code designed to meet the growing computational demands of next-generation HEP experiments. It provides efficient simulation of 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. |
| title | Accelerating detector simulations with Celeritas: profiling and performance optimizations |
| topic | Computational Physics |
| url | https://arxiv.org/abs/2503.17608 |