A particle packing parallel geometric method using GPU

The purpose of this paper is to present a methodology for obtaining granular models from a GPU parallel implementation of the geometric separation particle packing strategy. This methodology is suitable for the generation of large-scale granular models used in discontinuous media simulations. The proposed approach uses disk-shaped particles (two-dimensional approach) and parallelization mechanisms that consider different computational environments, with a focus on GPUs. The methodology is divided into three macro-steps: (a) definition of an input set of particles; (b) geometric separation; and (c) removal of spurious particles. The set of input particles uses data related to the particle size distribution and the domain filling rate, defining arbitrary positions for the particles. The other steps are used to eliminate overlaps between particles. Parallel computing is performed using the OpenCL programming API on compatible devices. Examples are presented to show the effectiveness of the proposed methodology. They show good time improvement and better memory efficiency in comparison with the original serial version of the strategy. The method is also validated by comparing the results with experimental and numerical data from the literature. The proposed methodology allows generating granular models with a parallel GPU particle packing method. It turns possible the achievement of bigger models in a smaller amount of time, without compromising the strategy efficiency and accuracy. It also presents mechanisms to avoid information exchange between GPU and CPU.

Automated summary

The paper presents a methodology for obtaining granular models from a GPU parallel implementation of the geometric separation particle packing strategy, suitable for large-scale models in discontinuous media simulations. This approach uses disk-shaped particles and parallelization mechanisms, demonstrating good time improvement and memory efficiency compared to the serial version. It is validated by comparing results with experimental and numerical data, allowing the generation of bigger models in a shorter amount of time without compromising efficiency and accuracy.