  Micro-macro models provide a powerful tool to study the relationship between
microscale mechanisms and emergent macroscopic behavior. However, the detailed
microscopic modeling may require tracking and evolving a high-dimensional
configuration space at high computational cost. In this work, we present a
parallel algorithm for simulation a high-dimensional micro-macro model of a
gliding motility assay. We utilize a holistic approach aligning the data
residency and simulation scales with the hybrid CPU and multi-GPU hardware.
With a combination of algorithmic modifications, GPU optimizations, and scaling
to multiple GPUs, we achieve speedup factors of up to 27 over our previous
hybrid CPU-GPU implementation and up to 540 over our single-threaded
implementation. This approach enables micro-macro simulations of higher
complexity and resolution than would otherwise be feasible.
