Authors: Ji Qi; Kuan-Ching Li; Hai Jiang; Qingguo Zhou; Lei Yang
Addresses: Institute of Modern Physics, Chinese Academy of Sciences, 509 Nanchang Rd., Lanzhou 730000, China ' Department of Computer Science and Information Engineering (CSIE Dept.), Providence University, 200 Chung-Chi Road, Shalu, Taichung 43301, Taiwan ' Department of Computer Science, Computer Science/Mathematics, Room 127, P. O. Box 9, State University, AR 72467, USA ' Distributed and Embedded System Lab, SISE, Lanzhou University, Lanzhou, Gansu, 730000, China ' Institute of Modern Physics, Chinese Academy of Sciences, 509 Nanchang Rd., Lanzhou 730000, China
Abstract: Granular materials are considered as the most seen materials in the world, and discrete element method (DEM) has become one of the most accurate and effective methods to simulate them. However, to achieve the preciseness expected from DEM, there exist huge force computations. Researchers have to either focus on simulations with fewer particles or build large-scale computer clusters for the ones with more particles. Moreover, DEM exhibits rich data-parallel nature in simulations. Recently, graphics processing units (GPU) have become yet another powerful parallel computing platform for scientific applications. In this paper, we intend to implement DEM on GPUs to explore system resources thoroughly for performance gains. Experiment results have demonstrated that the proposed implementation can achieve 2x~15x speedup depending on the number of particles and generations of GPUs, when compared to LAMMPS/granular module on 4-core systems.
Keywords: discrete element method; DEM; graphics processing units; GPUs; neighbour list; linked cells; CUDA; granular materials; parallel computing.
International Journal of Computational Science and Engineering, 2015 Vol.11 No.3, pp.330 - 337
Available online: 23 Oct 2015 *Full-text access for editors Access for subscribers Purchase this article Comment on this article