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The aim is to design a framework that optimizes computational resources for population health simulations using CPU-GPU architecture.

Enable : A CPU-GPU framework for dynamic data-driven agent-based population health simulations

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The aim is to design a framework that optimizes computational resources for population health simulations using CPU-GPU architecture.
Developed a hybrid agent-based modeling framework, Enable, for the Frontier supercomputer.
Generated contact networks directly from activity schedules without precomputed graphs.
Evaluated performance using synthetic and real-world datasets with up to 1200 GPUs.
Achieved high parallel efficiency with balanced edge distributions across processors.
Demonstrated uniform execution times in large-scale simulations.
Improved scaling performance for city-to-national scale populations.

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Randomized trial evaluates high-fidelity population health simulations in large-scale models, suggesting enhanced computational efficiency.

Alam et al. (2026) studied this question.

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Randomized trial evaluates high-fidelity population health simulations in large-scale models, suggesting enhanced computational efficiency.