What question did this study set out to answer?

This research aims to enhance track reconstruction techniques using Graph Neural Networks for the ATLAS Trigger System.

February 5, 2026

Performance of the ATLAS GNN4ITk Particle Track Reconstruction GPU pipeline

Key Points

This research aims to enhance track reconstruction techniques using Graph Neural Networks for the ATLAS Trigger System.
Exploration of Graph Neural Networks for track reconstruction
Utilization of Graphics Processing Units for efficiency
Reduction of memory consumption and inference time for improved performance
Achieved high efficiency in track reconstruction
Reduced memory usage to facilitate costs in GPU implementation
Maintained superior tracking performance despite computational demands

Abstract

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.

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Performance of the ATLAS GNN4ITk Particle Track Reconstruction GPU pipeline

Key Points

Abstract

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