  The Exa.TrkX project has applied geometric learning concepts such as metric
learning and graph neural networks to HEP particle tracking. Exa.TrkX's
tracking pipeline groups detector measurements to form track candidates and
filters them. The pipeline, originally developed using the TrackML dataset (a
simulation of an LHC-inspired tracking detector), has been demonstrated on
other detectors, including DUNE Liquid Argon TPC and 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.
