Speaker
Description
Increases in instantaneous luminosity and detector granularity will increase the amount of data that has to be analyzed by high-energy physics experiments, whether in real time or offline, by an order of magnitude. In this context, Graph Neural Networks have received a great deal of attention in the community for the reconstruction of charged particles, because their computational complexity scales linearly with the number of hits in the detector. We present a GNN reconstruction of LHCb’s vertex detector and benchmark its computational performance on both GPU and CPU architectures. A unique aspect of our work is the integration into LHCb's fully GPU-based first-level trigger system, Allen, which performs at the rate up to 30 MHz in the ongoing Run~3. Our work is the first attempt to operate a GNN charged particle reconstruction in such a high-throughput environment using GPUs, and we discuss the pros and cons of the GNN and classical algorithms in a detailed like-for-like comparison.
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