GPU-based parallel computation for structural dynamic response analysis with CUDA

被引:4
作者
Kang, Dong-Keun [1 ]
Kim, Chang-Wan [2 ]
Yang, Hyun-Ik [3 ,4 ]
机构
[1] Hanyang Univ, Dep Mech Design Engn, Seoul 133791, South Korea
[2] Konkuk Univ, Sch Mech Engn, Seoul 143701, South Korea
[3] Hanyang Univ, Dept Mech Engn, Ansan 426791, Kyeonggi Do, South Korea
[4] Hanyang Univ, Ansan 426791, Kyeonggi Do, South Korea
基金
新加坡国家研究基金会;
关键词
CUDA; FEM; Frequency response analysis; GPU; Parallel processing; Sparse conjugate gradient method; CONJUGATE-GRADIENT METHOD; LATTICE-BOLTZMANN; IMPLEMENTATION; PERFORMANCE; ALGORITHM;
D O I
10.1007/s12206-014-0928-2
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
Frequency response analysis is an important computational tool to simulate and understand the dynamic behavior of structures. However, for more target frequency and/or larger scale structures, the runtime is greatly increased. Furthermore, increasingly complex degree of freedom problems intended to improve the accuracy of the analysis results is creating longer. In this paper, we present efficient analysis using runtime reduction in frequency response analysis with NVIDIA GPU using the compute unified device architecture (CUDA) programming environment. The proposed method is based on the sparse conjugate gradient method and a Jacobi preconditioner. Numerical examples which implemented by three different FE model are used to verify the validity. The results show that GPU parallel implementation achieves significant speed up compared to a single CPU processor. Through these results, in the frequency response analysis, we show the possibility for efficient analysis with reduction of the solving time by using GPU parallel implementation.
引用
收藏
页码:4155 / 4162
页数:8
相关论文
共 30 条
[11]  
Golub G. H., 1996, MATRIX COMPUTATIONS
[12]   Parallel preconditioned conjugate gradient algorithm on GPU [J].
Helfenstein, Rudi ;
Koko, Jonas .
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS, 2012, 236 (15) :3584-3590
[13]   Frequency response computation of structures including non-proportional damping in a shared memory environment [J].
Jeong, T. G. ;
Lee, S. S. ;
Kim, Chang-Wan .
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE, 2013, 227 (C2) :288-298
[14]   Real-time nonlinear finite element computations on GPU - Application to neurosurgical simulation [J].
Joldes, Grand Roman ;
Wittek, Adam ;
Miller, Karol .
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 2010, 199 (49-52) :3305-3314
[15]   CFD-based analysis and two-level aerodynamic optimization on graphics processing units [J].
Kampolis, I. C. ;
Trompoukis, X. S. ;
Asouti, V. G. ;
Giannakoglou, K. C. .
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 2010, 199 (9-12) :712-722
[16]   Use of distributed-memory parallel processing in computing the dynamic response of the passenger-car system [J].
Kim, Chang-wan .
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING, 2006, 220 (D10) :1373-1381
[17]  
Kincaid D., 2002, Numerical Analysis: Mathematics of Scientific Computing
[18]   LBM based flow simulation using GPU computing processor [J].
Kuznik, Frederic ;
Obrecht, Christian ;
Rusaouen, Gilles ;
Roux, Jean-Jacques .
COMPUTERS & MATHEMATICS WITH APPLICATIONS, 2010, 59 (07) :2380-2392
[19]  
Meirovitch L., 1967, Analytical methods in vibrations
[20]  
Naumov M., 2011, Parallel Solution of Sparse Triangular Linear Systems in the Preconditioned Iterative Methods on the GPU