Speakers
Description
Graph-based reconstruction methods are well-suited to the sparse and irregular geometry of modern calorimeters, but their deployment often depends on achieving low and predictable inference latency across heterogeneous computing environments. We evaluate GarNet, a lightweight Graph Neural Network (GNN) for calorimeter energy reconstruction, focusing on its cross-backend performance using PyTorch and ONNX Runtime on both multi-core CPUs and NVIDIA A100 GPUs. For small graph inputs representative of PicoCal deposits, ONNX Runtime delivers up to 5× faster CPU and nearly 2× faster GPU inference than Pytorch Runtime while maintaining FP32 numerical agreement at the 10⁻⁷ level.
This work is motivated by real-time deployment within the HLT1 reconstruction stage of the LHCb trigger, where the fully GPU-based Allen framework performs low-latency event processing to select interesting collisions at the LHC bunch-crossing rate. Integrating fast, portable ONNX-accelerated GarNet inference into this environment would enable graph-based calorimeter reconstruction to contribute directly to real-time decision-making in future high-rate running.