We discuss modeling, algorithmic, and software aspects that allow a simulation tool called Chrono::Granular to run billion-degree-of-freedom dynamics problems on commodity hardware, i.e., a workstation with one GPU. The ability to scale the solution to large problem sizes is traced back to an adimensionalization process combined with the use of mixed-precision data types that reduce memory pressure and improve arithmetic intensity, judicious use of the memory ecosystem on GPU cards as exposed by CUDA on Nvidia architectures, and a software implementation that prioritizes execution speed over modeling generality. The simulation approach is demonstrated for 3D scenarios with up to 710 million bodies for the frictionless case (of relevance in emulsions), and up to 210 million bodies for scenarios with friction (of relevance in terradynamics, additive manufacturing, soft-matter physics). The frictional contact model used draws on the Discrete Element Method (DEM). A performance benchmark shows linear scaling with problem size up to GPU memory capacity. The implementation has an application programming interface that enables it to interact in a cosimulation framework with third-party dynamics engines. This interaction is anchored by a force–displacement data exchange protocol that brings in external bodies as geometries defined by triangle meshes. We demonstrate the cosimulation mechanism by interfacing to an open source, multiphysics simulation engine called Chrono. Therein, triangular meshes define moving boundary conditions for Chrono::Granular, which in turn provides forces and torques acting on the triangular meshes. Several tests are considered for validation and scaling analysis purposes. The limiting aspects of the current implementation are its exclusive support of monodisperse granular systems, and its lack of handling geometries beyond spheres. These limitations are addressed by ongoing work.

我们讨论了建模，算法和软件方面的问题，这些方面使称为Chrono :: Granular的仿真工具可以在商品硬件（即带有一个GPU的工作站）上运行十亿自由度的动力学问题。将解决方案扩展到大问题的能力可以追溯到一个维度化过程，并使用混合精度数据类型来减少内存压力并提高算术强度，明智地使用CUDA所揭示的GPU卡上的内存生态系统Nvidia体系结构以及优先于模型通用性的执行速度的软件实现。在3D场景中演示了仿真方法，其中无摩擦情况（与乳液相关）有7.1亿个物体，而摩擦系数（与地形动力学相关）有2.1亿个物体。增材制造，软物质物理学）。所使用的摩擦接触模型基于离散元方法（DEM）。性能基准测试显示线性扩展，问题大小最大可达到GPU内存容量。该实现具有一个应用程序编程接口，使它能够与第三方动力学引擎在协同仿真框架中进行交互。这种相互作用以力-位移数据交换协议为基础，该协议将外部物体引入由三角形网格定义的几何形状。我们通过与称为Chrono的开源多物理场仿真引擎进行交互来演示协同仿真机制。其中，三角形网格定义了Chrono :: Granular的移动边界条件，进而提供了作用在三角形网格上的力和扭矩。为了验证和缩放分析目的，考虑了几种测试。当前实施方式的局限性在于它对单分散颗粒系统的独家支持，并且缺乏处理超出球体的几何形状的能力。这些限制可以通过正在进行的工作来解决。