The HL-LHC upgrade of the ATLAS inner detector (ITk) brings an unprecedented challenge, both in terms of the large number of silicon cluster readouts and the throughput required for budget-constrained track reconstruction. Applying Graph Neural Networks (GNNs) has been shown to be a promising solution to this problem with competitive physics performance at sub-second inference time. In this contribution, the expected physics performance of the GNN4ITk track reconstruction chain will be presented, with emphasis on improvements in efficiency, fake rate, and track parameter resolution from recent developments in graph segmentation and treatment of track candidates. Results from first studies on not yet covered topics such as electron reconstruction and stability against detector defects will be shown. Apart from that, the presentation will highlight recent improvements on the computational performance of the pipeline. This includes machine learning model optimizations with a focus on inference acceleration, ranging from mixed precision and model reduction to industry-grade compilation solutions, as well as refinement of the graph-building cuts that reduce the timing without significant loss in reconstruction performance. Furthermore, dedicated CUDA kernels to accelerate the graph-building and the graph-segmentation timings have been implemented and optimized. Finally, the recent progress in integrating the GNN pipeline into the ATLAS software infrastructure and first studies on throughput in this setup environment will be shown.Scientific contact person Juste Rozas, Aurelio, (aurelio.juste.rozas@cern.ch)
ATLAS Collaboration (Wed,) studied this question.