HEP advanced tracking algorithms
at the exascale (Project Exa.TrkX)
Reconstructing the trajectories of thousands of charged particles from a collision event as they fly through a HEP detector
is a combinatorially hard pattern recognition problem. Exa.TrkX, a DOE CompHEP project and a collaboration of data scientists and computational
physicists from the ATLAS, CMS, and DUNE experiments, is developing Graph Neural Network models aimed at reconstructing millions of particle trajectories per second
from Petabytes of raw data produced by the next generation of detectors at the Energy and Intensity Frontiers.
Exa.TrkX is also exploring the scaling of distributed training of GNN models on DOE pre-exascale systems and the deployment
of GNN models with microsecond latencies on FPGA-based real-time processing systems.
Exa.TrkX is a follow-up to the the HEP.TrkX pilot project
It relies on the aCTS
toolkit to simulate a generic HL-LHC detector,
and more recently to benchmark the performance of its models.
Exa.TrkX is collaborating with the FastML Lab
to deploy GNN models
on FPGA systems. Exa.TrkX is also collaborating with the NERSC Big Data center
and the Exalearn co-design center
to demonstrate distributed training and model hyperparameter optimization at scale on HPC systems.
collaborators, partners & toolkits
reports, publications & presentations