  The Exa.TrkX project has applied geometric learning concepts such as metric
learning and graph neural networks to HEP particle tracking. The Exa.TrkX
tracking pipeline clusters detector measurements to form track candidates and
filters them. The pipeline, originally developed using the TrackML dataset (a
simulation of an LHC-like tracking detector), has been demonstrated on various
detectors, including the DUNE LArTPC and the CMS High-Granularity Calorimeter.
This paper documents new developments needed to study the physics and computing
performance of the Exa.TrkX pipeline on the full TrackML dataset, a first step
towards validating the pipeline using ATLAS and CMS data. The pipeline achieves
tracking efficiency and purity similar to production tracking algorithms.
Crucially for future HEP applications, the pipeline benefits significantly from
GPU acceleration, and its computational requirements scale close to linearly
with the number of particles in the event.
