Development of a GPGPU‐parallelized hybrid finite‐discrete element method for modeling rock fracture

• Published in: International journal for numerical and analytical methods in geomechanics Vol. 43; no. 10; pp. 1797 - 1824
• Main Authors: Fukuda, Daisuke, Mohammadnejad, Mojtaba, Liu, Hongyuan, Dehkhoda, Sevda, Chan, Andrew, Cho, Sang‐Ho, Min, Gyeong‐Jo, Han, Haoyu, Kodama, Jun‐ichi, Fujii, Yoshiaki
• Format: Journal Article
• Language: English
• Published: Bognor Regis Wiley Subscription Services, Inc 01.07.2019
• Subjects: Accelerators; Algorithms; Computer architecture; Computer simulation; Contact stresses; CUDA C/C++; Damping; Detection; Discrete element method; Dynamic loads; fracture process analysis; Geostatics; Glycoprotein gp100; GPGPU; Graphics; Graphics processing units; hybrid FDEM; impact loading; Mechanical loading; Parallel processing; quasi‐static loading; Rocks; Stress analysis; Theoretical analysis
• ISSN: 0363-9061; 1096-9853
• DOI: 10.1002/nag.2934

Summary The hybrid finite‐discrete element method (FDEM) is widely used for engineering applications, which, however, is computationally expensive and needs further development, especially when rock fracture process is modeled. This study aims to further develop a sequential hybrid FDEM code formerly proposed by the authors and parallelize it using compute unified device architecture (CUDA) C/C++ on the basis of a general‐purpose graphics processing unit (GPGPU) for rock engineering applications. Because the contact detection algorithm in the sequential code is not suitable for GPGPU parallelization, a different contact detection algorithm is implemented in the GPGPU‐parallelized hybrid FDEM. Moreover, a number of new features are implemented in the hybrid FDEM code, including the local damping technique for efficient geostatic stress analysis, contact damping, contact friction, and the absorbing boundary. Then, a number of simulations with both quasi‐static and dynamic loading conditions are conducted using the GPGPU‐parallelized hybrid FDEM, and the obtained results are compared both quantitatively and qualitatively with those from either theoretical analysis or the literature to calibrate the implementations. Finally, the speed‐up performance of the hybrid FDEM is discussed in terms of its performance on various GPGPU accelerators and a comparison with the sequential code, which reveals that the GPGPU‐parallelized hybrid FDEM can run more than 128 times faster than the sequential code if it is run on appropriate GPGPU accelerators, such as the Quadro GP100. It is concluded that the GPGPU‐parallelized hybrid FDEM developed in this study is a valuable and powerful numerical tool for rock engineering applications.