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Micro-macro models provide a powerful tool to study the relationship betweenmicroscale mechanisms and emergent macroscopic behavior. However, the detailedmicroscopic modeling may require tracking and evolving a high-dimensionalconfiguration space at high computational cost. In this work, we present aparallel algorithm for simulation a high-dimensional micro-macro model of agliding motility assay. We utilize a holistic approach aligning the dataresidency and simulation scales with the hybrid CPU and multi-GPU hardware.With a combination of algorithmic modifications, GPU optimizations, and scalingto multiple GPUs, we achieve speedup factors of up to 27 over our previoushybrid CPU-GPU implementation and up to 540 over our single-threadedimplementation. This approach enables micro-macro simulations of highercomplexity and resolution than would otherwise be feasible.

Enabling Simulation of High-Dimensional Micro-Macro Biophysical Models through Hybrid CPU and Multi-GPU Parallelism