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Parallelization of torsion finite element code using compressed stiffness matrix algorithm
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DOI：10.1007/s00366-020-00952-w.png)
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摘要
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In the current study, the problems of elastic and elastoplastic torsion were formulated by finite element method. The finite element code was parallelized on both shared and distributed memory architectures. An assembling method with high parallelism ability and consuming minimum memory was proposed to obtain compressed global stiffness matrix directly. Parallel programming principles were expressed in two shared memory and distributed memory approaches; moreover, parallel well-known mathematical libraries were briefly expressed. In this paper, the main focus was on a lucid explanation of parallelization mechanisms in detail on two memory architectures such as some settings of Linux operating system for large-scale problems. To verify the ability of the proposed method and its parallel performance, several benchmark examples were represented with different mesh sizes and were compared with their respective analytical solutions. Considering the obtained results, the proposed sparse assembling algorithm decreased required memory significantly (about 10(3.5) to 10(5.5) times) and the obtained speedup was about 3.4 for the elastoplastic torsion problem in a simple multicore computer.
Keyword:
Parallelization
OpenMP
SuperLU
Assembling
Finite element method
Elastoplastic torsion
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