  As unmanned aircraft systems (UASs) continue to integrate into the U.S.
National Airspace System (NAS), there is a need to quantify the risk of
airborne collisions between unmanned and manned aircraft to support regulation
and standards development. Developing and certifying collision avoidance
systems often rely on the extensive use of Monte Carlo collision risk analysis
simulations using probabilistic models of aircraft flight. To train these
models, high performance computing resources are required. We've prototyped a
high performance computing workflow designed and deployed on the Lincoln
Laboratory Supercomputing Center to process billions of observations of
aircraft. However, the prototype has various computational and storage
bottlenecks that limited rapid or more comprehensive analyses and models. In
response, we have developed a novel workflow to take advantage of various job
launch and task distribution technologies to improve performance. The workflow
was benchmarked using two datasets of observations of aircraft, including a new
dataset focused on the environment around aerodromes. Optimizing how the
workflow was parallelized drastically reduced the execution time from weeks to
days.
