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 9.25× 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.

中文翻译：

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通过混合 CPU 和多 GPU 并行实现高维微观宏观生物物理模型的模拟

微观宏观模型为研究微观机制与突发宏观行为之间的关系提供了强大的工具。然而，详细的微观建模需要以高计算成本跟踪和演化潜在的高维配置空间。在这项工作中，我们提出了一种新颖的并行算法，用于模拟滑翔运动试验的高维微观宏观模型。我们利用整体方法将数据驻留和模拟规模与混合 CPU 和多 GPU 硬件保持一致。我们的新方法在相同硬件上实现了 9.25 倍于以前 GPU 加速微宏方法的加速因子。此外，通过解耦微结构更新中的依赖关系，我们能够以最小的开销有效地将微结构分布在多个 GPU 上。我们在多达四个 GPU 上进行测试并观察到出色的扩展性，这表明使用额外的 GPU 可以实现显着的进一步加速。我们的方法可以实现比其他方法可行的复杂度和分辨率更高的微观宏观模拟。