Multi-GPUs DEM algorithm and its application in the simulation of granular materials - Zhou, Qian; Xu, Wen-Jie; Chen, Yong-Zhi (Powder technology, 2023)
The Discrete Element Method (DEM) is one of the most common numerical approach for modeling the physical and mechanical properties of granular materials. However, its computational efficiency can be a major bottleneck, which significantly hinders its application in engineering fields. To overcome this challenge, a multi-GPU framework with OpenMP has been developed. In this framework, the DEM particles in 3D space and their corresponding contact information are sorted in a given direction in the simulation, and each GPU device handles a subdomain of particles and relative contacts. Additionally, the contact information is densely arranged to minimize memory usage. To further enhance efficiency, sorting and packing algorithms for the contact information have been introduced. During calculating loop, the number of contacts on each device is monitored, and the spatial division of devices is adjusted to balance memory distribution. The efficacy of the multi-GPU algorithm is verified through a chute flow test. Using numerical results, the segregation process and mechanics of granular materials during sliding have been studied in detail. Furthermore, the simulations demonstrate that the developed multi-GPU algorithm greatly enhances computational efficiency and reduces memory usage per device. [Display omitted] •A multi-GPUs DEM framework is developed in this study.•The sorting and compression algorithms of contact information for Multi-GPUs DEM are provided.•The efficiency of multi-GPUs algorithm is verified by a chute flow test.•The segregation and mechanics of granular materials are studied.•The algorithm greatly improves the computation efficiency and decrease memory of per device.