  Recently, graph neural networks (GNNs) have been successfully used for a
variety of particle reconstruction problems in high energy physics, including
particle tracking. The Exa.TrkX pipeline based on GNNs demonstrated promising
performance in reconstructing particle tracks in dense environments. It
includes five discrete steps: data encoding, graph building, edge filtering,
GNN, and track labeling. All steps were written in Python and run on both GPUs
and CPUs. In this work, we accelerate the Python implementation of the pipeline
through customized and commercial GPU-enabled software libraries, and develop a
C++ implementation for inferencing the pipeline. The implementation features an
improved, CUDA-enabled fixed-radius nearest neighbor search for graph building
and a weakly connected component graph algorithm for track labeling. GNNs and
other trained deep learning models are converted to ONNX and inferenced via the
ONNX Runtime C++ API. The complete C++ implementation of the pipeline allows
integration with existing tracking software. We report the memory usage and
average event latency tracking performance of our implementation applied to the
TrackML benchmark dataset.
