50 million atoms scale molecular dynamics modelling on a single consumer graphics card. (October 2018)

• Record Type: Journal Article
• Authors: Xiao, Gaobo; Ren, Mingjun; Hong, Haibo
• Is Part Of: Advances in engineering software. Volume 124(2018)
• Page Start: 66
• Page End: 72
• Publication Date: 2018-10
• Subjects: Molecular dynamics -- parallel computing -- Graphics processing unit (GPU) -- Compute unified device architecture (CUDA) -- dynamic cell list; Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2018.08.004
• Languages: English
• ISSNs: 0965-9978

Abstract

Highlights: A dynamic cell-list method for implementing MD on GPU is proposed. Only cells containing atoms consume device memory and no neighbor list. 50 million atoms can be computed on a single consumer graphics card. 20 times number of atoms and 2.5 times speed of Lammps GPU package. Abstract: This paper presents a dynamic cell-list method for realizing large scale molecular dynamics (MD) simulations with more than 50 million atoms on a single consumer graphics card. It adapts the cell-list algorithm by introducing an efficient two-step atom location scheme and a dynamic memory allocation scheme such that only those cells containing atoms consume device memory. In addition, a large amount of memory is saved since it does not use the neighbour list. The computational efficiency is improved by reducing the memory loading times and maximizing coalesced memory access as compared to methods utilizing neighbour lists, since memory bandwidth is becoming the bottle-neck of the latest GPUs. As a result, MD simulations with more than 50 million atoms utilizing advanced three-body interaction potential are made possible on a consumer graphics card with just 11 GB of graphics memory. The proposed framework is designed to run totally on the graphics card, with all the data stored in the graphics memory to avoid the time-consuming data transfer between host and device. It achieves 2.5 times the speed and 20 times the atom number of the latest Lammps GPU package on the NVIDIA GTX 1080Ti GPU. The proposed framework is expected to help adapt existing MD packages for supporting large scale MD simulations on personal desktops and thereby extend MD to a wider range of researchers and engineers.