Enabling simulation of high-dimensional micro-macro biophysicalmodels through hybrid CPU and multi-GPU parallelism

摘要

Micro-macro models provide a powerful tool to study the relationship between microscale mechanisms and emergent macroscopic behavior. However, the detailed microscopic modeling requires tracking and evolving a potentially high-dimensional configuration space at high computational cost. In this work, we present a novel parallel algorithm for simulating 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. Our novel approach achieves a speedup factor of 9.25x over previous GPU-accelerated micro-macro methods on the same hardware. Furthermore, by decoupling dependencies in the microstructure update, we are able to efficiently distribute the microstructure over multiple GPUs with minimal overhead. We test on up to four GPUs and observe excellent scaling, suggesting that significant further speedups are achievable with additional GPUs. Our approach enables micro-macro simulations of higher complexity and resolution than would otherwise be feasible.