A parallel finite element procedure for contact-impact problems using edge-based smooth triangular element and GPU
A parallel finite element procedure for contact-impact problems using edge-based smooth triangular element and GPU
复制标题
使用基于边缘的平滑三角形单元和 GPU 解决接触冲击问题的并行有限元程序
DOI：
10.1016/j.cpc.2017.12.006
复制
发表时间：
2018-04-01
影响因子：
6.3
通讯作者：
Liu, Wenyang
中科院分区：
文献类型：
--
作者：
Cai, Yong;Cui, Xiangyang;Liu, Wenyang
边缘光滑有限元法（ES-FEM）可以提高三角壳单元的计算精度和复杂模型的网格划分效率。本文提出了一种基于ES-FEM的特殊边缘光滑三角形壳单元，利用图形处理单元（GPU）对接触-冲击问题进行显式有限元模拟的方法。对于这个问题至关重要的是实现更细粒度的并行性，以实现有效的数据加载并最大限度地减少设备和主机之间的通信。为了有效求解这些基于ES-FEM的壳单元公式，提出了四种并行策略，并采用了多种优化方法来保证内存访问的对齐。特别的重点是致力于开发一种方法的并行结构的边缘系统。该并行显式算法中嵌入了一种并行层次区域接触搜索算法（HITA）和一种并行罚函数计算方法。最后，设计了程序流程，并利用Nvidia的CUDA开发了基于gpu的仿真系统。最后给出了几个数值算例，以说明所提出方法所得到的结果是高质量的。此外，基于gpu的并行计算可以显著减少计算时间。(C) 2017 Elsevier B.V.版权所有
The edge-smooth finite element method (ES-FEM) can improve the computational accuracy of triangular shell elements and the mesh partition efficiency of complex models. In this paper, an approach is developed to perform explicit finite element simulations of contact-impact problems with a graphical processing unit (GPU) using a special edge-smooth triangular shell element based on ES-FEM. Of critical importance for this problem is achieving finer-grained parallelism to enable efficient data loading and to minimize communication between the device and host. Four kinds of parallel strategies are then developed to efficiently solve these ES-FEM based shell element formulas, and various optimization methods are adopted to ensure aligned memory access. Special focus is dedicated to developing an approach for the parallel construction of edge systems. A parallel hierarchy-territory contact-searching algorithm (HITA) and a parallel penalty function calculation method are embedded in this parallel explicit algorithm. Finally, the program flow is well designed, and a GPU-based simulation system is developed, using Nvidia's CUDA. Several numerical examples are presented to illustrate the high quality of the results obtained with the proposed methods. In addition, the GPU-based parallel computation is shown to significantly reduce the computing time. (C) 2017 Elsevier B.V. All rights reserved.