Simulation of in vivo cellular processes with the reaction-diffusion master equation (RDME) is a computationally expensive task. Our previous software enabled simulation of inhomogeneous biochemical systems for small bacteria over long time scales using the MPD-RDME method on a single GPU. Simulations of larger eukaryotic systems exceed the on-board memory capacity of individual GPUs, and long time simulations of modest-sized cells such as yeast are impractical on a single GPU. We present a new multi-GPU parallel implementation of the MPD-RDME method based on a spatial decomposition approach that supports dynamic load balancing for workstations containing GPUs of varying performance and memory capacity. We take advantage of high-performance features of CUDA for peer-to-peer GPU memory transfers and evaluate the performance of our algorithms on state-of-the-art GPU devices. We present parallel e ciency and performance results for simulations using multiple GPUs as system size, particle counts, and number of reactions grow. We also demonstrate multi-GPU performance in simulations of the Min protein system in E. coli. Moreover, our multi-GPU decomposition and load balancing approach can be generalized to other lattice-based problems.

中文翻译：

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使用多 GPU 工作站模拟生物相关大小和时间尺度上的反应扩散过程。

使用反应扩散主方程 (RDME) 模拟体内细胞过程是一项计算量大的任务。我们之前的软件能够在单个 GPU 上使用 MPD-RDME 方法在长时间尺度上模拟小细菌的非均质生化系统。大型真核系统的模拟超出了单个 GPU 的机载内存容量，而在单个 GPU 上对中等大小的细胞（如酵母）进行长时间模拟是不切实际的。我们提出了一种基于空间分解方法的 MPD-RDME 方法的新多 GPU 并行实现，该方法支持包含具有不同性能和内存容量的 GPU 的工作站的动态负载平衡。我们利用 CUDA 的高性能特性进行对等 GPU 内存传输，并评估我们算法在最先进的 GPU 设备上的性能。随着系统大小、粒子数和反应次数的增加，我们展示了使用多个 GPU 进行模拟的并行效率和性能结果。我们还在大肠杆菌中的 Min 蛋白质系统的模拟中展示了多 GPU 性能。此外，我们的多 GPU 分解和负载平衡方法可以推广到其他基于格的问题。