Researchers in biology are faced with the tough challenge of developing high-performance computer simulations of their increasingly complex agent-based models. BioDynaMo is an open-source agent-based simulation platform that aims to alleviate researchers from the intricacies that go into the development of high-performance computing. Through a high-level interface, researchers can implement their models on top of BioDynaMo's multi-threaded core execution engine to rapidly develop simulations that effectively utilize parallel computing hardware. In biological agent-based modeling, the type of operations that are typically the most compute-intensive are those that involve agents interacting with their local neighborhood. In this work, we investigate the currently implemented method of handling neighborhood interactions of cellular agents in BioDynaMo, and ways to improve the performance to enable large-scale and complex simulations. We propose to replace the kd-tree implementation to find and iterate over the neighborhood of each agent with a uniform grid method that allows us to take advantage of the massively parallel architecture of graphics processing units (GPUs). We implement the uniform grid method in both CUDA and OpenCL to address GPUs from all major vendors and evaluate several techniques to further improve the performance. Furthermore, we analyze the performance of our implementations for models with a varying density of neighboring agents. As a result, the performance of the mechanical interactions method improved by up to two orders of magnitude in comparison to the multithreaded baseline version. The implementations are open-source and publicly available on Github.

中文翻译：

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基于3D Agent的生物仿真的GPU加速

生物学研究人员面临着为其日益复杂的基于代理的模型开发高性能计算机仿真的艰巨挑战。BioDynaMo是一个基于开源代理的模拟平台，旨在减轻研究人员对高性能计算开发的复杂性。通过高级界面，研究人员可以在BioDynaMo的多线程核心执行引擎之上实现他们的模型，从而快速开发有效利用并行计算硬件的仿真。在基于生物代理的建模中，通常最耗费计算资源的操作类型是那些涉及代理与其本地邻域进行交互的操作。在这项工作中，我们研究了当前实现的在BioDynaMo中处理细胞因子邻域相互作用的方法，以及改善性能以实现大规模和复杂模拟的方法。我们建议用统一的网格方法替换kd-tree实现，以查找并遍历每个代理的邻域，从而使我们能够利用图形处理单元（GPU）的大规模并行体系结构。我们在CUDA和OpenCL中都实现了统一网格方法，以解决所有主要供应商的GPU，并评估了几种技术来进一步提高性能。此外，我们分析了具有不同密度的相邻代理的模型的实现性能。因此，与多线程基准版本相比，机械交互方法的性能提高了两个数量级。这些实现是开源的，可以在Github上公开获得。