列车-轨道-地基土耦合系统三维随机振动的多GPU并行计算方法
A multi-GPU parallel computing method for 3D random vibration of train-track-soil dynamic interaction
朱志辉 1杨啸 2李昊 3徐海坤 4邹有4
作者信息
- 1. National Engineering Research Center of High-speed Railway Construction Technology,Central South University,Changsha 410075,China;School of Civil Engineering,Central South University,Changsha 410075,China
- 2. National Engineering Research Center of High-speed Railway Construction Technology,Central South University,Changsha 410075,China
- 3. School of Computer Science and Engineering, Central South University, Changsha 410083, China
- 4. Information and Network Center,Central South University,Changsha 410083,China
- 折叠
摘要
针对列车-轨道-地基土耦合系统随机计算效率低的问题,本文提出了基于多GPU的列车-轨道-地基土随机振动方程的高效并行计算方法.基于OpenMP-CUDA混编技术将虚拟激励法不同频点下的多个线性方程组求解任务分配给多个GPU并行执行;在每块GPU上,采用基于CUDA的预处理共轭梯度法(PCG)并行求解对称正定的等效静力平衡方程.针对耦合系统等效刚度矩阵的稀疏特性,采用行压缩(CSR)格式存储大型稀疏矩阵以节省内存空间.最终通过MATLAB-CUDA混合平台开发并行计算程序,解决了随机振动分析中多个线性方程组串行求解效率低的难题.数值算例表明,基于四GPU节点的多GPU并行算法和单GPU加速PCG算法的计算效率是串行多点同步算法(MPSA)计算效率的22.59倍和3.75倍.
Abstract
In this paper,an efficient computation method based on a multi-GPU parallel algorithm is proposed to overcome the low efficiency in random calculation of the train-track-soil coupled system(TTSCS).Firstly,for the large time consumption caused by solving multiple independent equations of TTSCS at different frequency points in serially random vibration analysis,the multi-GPU parallel algorithm is proposed and programmed based on the OpenMP-CUDA algorithm.The tasks of solving multiple linear equations for random vibration analysis of the TTSCS are distributed to different GPUs for parallel execution.On each GPU,the large sparse linear equations of TTSCS are solved by the CUDA-based parallel preconditioned conjugate gradient(PCG)method,and the large sparse matrix is stored in the compressed sparse row(CSR)format to save memory space.Then,the parallel computing program is implemented on the MATLAB-CUDA hybrid platform.Finally,numerical examples show that the efficiency of solving large sparse linear equations based on the multi-GPU parallel algorithm implemented on a 4-GPU node and the GPU-accelerated PCG algorithm implemented on a personal computer with a single GPU is 22.59 times and 3.75 times that of the multi-point synchronization algorithm(MPSA),respectively.
关键词
随机振动/并行计算/多GPU/三维有限元法/列车-轨道-地基土耦合模型Key words
random vibration/parallel computing/multi-GPU/three-dimensional finite element method/train-track-soil couple model引用本文复制引用
基金项目
National Natural Science Foundation of China(52078498)
Science and Technology Research and Development Program Project of China Railway Group Limited(2021-Special-04-2)
Natural Science Foundation of Hunan Province,China(2022JJ30745)
Frontier Cross Research Project of Central South University,China(2023QYJC006)
Hunan Provincial Science and Technology Promotion Talent Project,China(2020TJ-Q19)
出版年
2023
NETL
NSTL
维普
万方数据