Simulating large-scale, high-fidelity population health models demands immense computational resources and efficient parallelization strategies. We present the design and performance of Enable (Efficient National-scale Agent-Based Learning Environment), a hybrid CPU-GPU agent-based modeling framework optimized for the Frontier supercomputer. Enable generates contact networks directly from activity schedules, enabling location-based parallel execution without relying on precomputed contact graphs. A GPU device constructs contact networks and uses an efficient load balancing algorithm to assign locations to processors, while CPUs perform the simulation tasks. This design achieves high parallel efficiency by ensuring balanced edge distributions across processors, resulting in uniform execution times. We evaluate both strong and weak scaling using synthetic and real-world datasets generated from UrbanPop, Uber H3, and OSM maps. Scaling performance is studied with city-to national-scale population sizes on up to 1200 GPUs of the Frontier supercomputer. Enable addresses key computational challenges in large-scale high-fidelity agent-based simulations that beset the development of national-scale virtual population health twins.
Alam et al. (Wed,) studied this question.