| Issue |
EPJ Web Conf.
Volume 337, 2025
27th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2024)
|
|
|---|---|---|
| Article Number | 01043 | |
| Number of page(s) | 8 | |
| DOI | https://doi.org/10.1051/epjconf/202533701043 | |
| Published online | 07 October 2025 | |
https://doi.org/10.1051/epjconf/202533701043
Performance of the ATLAS GNN4ITk Particle Track Reconstruction GPU pipeline
1 CERN
2 Heidelberg University
* e-mail: aleksandra.poreba@cern.ch
** e-mail: holger.froening@ziti.uni-heidelberg.de
Published online: 7 October 2025
With the upcoming upgrade of High Luminosity LHC, existing algorithms of the ATLAS Trigger System will demand increasing computational power by more than an order of magnitude. Therefore, alternative reconstruction techniques are explored by the ATLAS collaboration, including the usage of Graph Neural Networks (GNN) for the track reconstruction. The project focusing on that research, GNN4ITk, considers several heterogeneous computing options, including the usage of Graphics Processing Units (GPU). The framework can reconstruct tracks with high efficiency, however, the computing requirements of the pipeline are high. We will report on the efforts to reduce the memory consumption and inference time enough to enable the usage of commercially available and affordable GPUs for the future ATLAS trigger system while maintaining high tracking performance.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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