Application of GPU-Based Computing to Large Scale Finite Element Analysis of Three-Dimensional Structures

Akbariyeh, Ashkan; Carrigan, Travis; Dennis, Brian H.; Chan, Wen S.; Wang, B. P.; Lawrence, K. L.

doi:10.4203/ccp.100.6

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…In the third category, Mueller et al [13], presented a matrix-free GPU implementation of a preconditioned CG solver for anisotropic elliptic partial differential equations. In [14], the performance of the GPU implementation of assembly-free FEA using trilinear hexahedral elements, was compared against the corresponding serial version run on a conventional processor for various mesh sizes and sparse matrix storage schemes.…”

Section: Direct and Iterative Solvers: Tradeoffmentioning

confidence: 99%

A Deflated Assembly Free Approach to Large-Scale Implicit Structural Dynamics

Mirzendehdel

Suresh

2015

Journal of Computational and Nonlinear Dynamics

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The primary computational bottle-neck in implicit structural dynamics is the repeated inversion of the underlying stiffness matrix. In this paper, a fast inversion technique is proposed by merging four distinct but complementary concepts: (1) voxelization with adaptive local refinement, (2) assembly-free (a.k.a. matrix-free or element-by-element) finite element analysis (FEA), (3) assembly-free deflated conjugate gradient (AF-DCG), and (4) multicore parallelization. In particular, we apply these concepts to the well-known Newmark-beta method, and the resulting AF-DCG is well-suited for large-scale problems. It can be easily ported to many-core central processing unit (CPU) and multicore graphics-programmable unit (GPU) architectures, as demonstrated through numerical experiments.

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Section: Direct and Iterative Solvers: Tradeoffmentioning

confidence: 99%

A Deflated Assembly Free Approach to Large-Scale Implicit Structural Dynamics

Mirzendehdel

Suresh

2015

Journal of Computational and Nonlinear Dynamics

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“…In this area the achieved acceleration of computing compared to CPU is from about 18x to 48x. There are solutions allowing the CUDA technology to be applied in conjunction with the finite element method, where as presented in paper [14], acceleration of computing is from 12x to 50x, depending on the algorithm executed. On the basis of the foregoing literature, one can state that the use of the CUDA architecture will allow the computing capacities of the numerical computing module, being part of a comprehensive system for ceramic mould quality forecasting, to be effectively increased.…”

Section: Introductionmentioning

confidence: 99%

Archives of Foundry Engineering

Hojny,

Żaba,

Dębiński

et al. 2020

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This paper presents practical capabilities of a system for ceramic mould quality forecasting implemented in an industrial plant (foundry). The main assumption of the developed solution is the possibility of eliminating a faulty mould from a production line just before the casting operation. It allows relative savings to be achieved, and faulty moulds, and thus faulty castings occurrence in the production cycle to be minimized. The numerical computing module (the DEFFEM 3D package), based on the smoothed particle hydrodynamics (SPH) is one of key solutions of the system implemented. Due to very long computing times, the developed numerical module cannot be effectively used to carry out multi-variant simulations of mould filling and solidification of castings. To utilize the benefits from application of the CUDA architecture to improve the computing effectiveness, the most time consuming procedure of looking for neighbours was parallelized (cell-linked list method). The study is complemented by examples of results of performance tests and their analysis.

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Application of GPU-Based Computing to Large Scale Finite Element Analysis of Three-Dimensional Structures

Cited by 2 publications

References 4 publications

A Deflated Assembly Free Approach to Large-Scale Implicit Structural Dynamics

A Deflated Assembly Free Approach to Large-Scale Implicit Structural Dynamics

Archives of Foundry Engineering

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