  This paper reports on the second "Throughput" phase of the Tracking Machine
Learning (TrackML) challenge on the Codalab platform. As in the first
"Accuracy" phase, the participants had to solve a difficult experimental
problem linked to tracking accurately the trajectory of particles as e.g.
created at the Large Hadron Collider (LHC): given O($10^5$) points, the
participants had to connect them into O($10^4$) individual groups that
represent the particle trajectories which are approximated helical. While in
the first phase only the accuracy mattered, the goal of this second phase was a
compromise between the accuracy and the speed of inference. Both were measured
on the Codalab platform where the participants had to upload their software.
The best three participants had solutions with good accuracy and speed an order
of magnitude faster than the state of the art when the challenge was designed.
Although the core algorithms were less diverse than in the first phase, a
diversity of techniques have been used and are described in this paper. The
performance of the algorithms are analysed in depth and lessons derived.
