GPU-accelerated DEM implementation with CUDA
by Ji Qi; Kuan-Ching Li; Hai Jiang; Qingguo Zhou; Lei Yang
International Journal of Computational Science and Engineering (IJCSE), Vol. 11, No. 3, 2015

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.

Online publication date: Fri, 23-Oct-2015

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computational Science and Engineering (IJCSE):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email