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CG-Based Subdomain Local Solver with ICT Factorization Preconditioner for Domain Decomposition Method

机译:基于CG的ICT分解预处理器的子域局部求解器,用于域分解方法

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References(16) To analyze large-scale problems by a domain decomposition method (DDM), it is important to accelerate the subdomain local solver. For utilizing cache memory effectively and for saving main memory usage, we employ the preconditioner of incomplete Cholesky factorization with threshold (ICT) optimized for the subdomain local solver of the DDM. Though the ICT preconditioner was originally proposed for ill-conditioned problems, we employ it in this study because it can freely control the number of nonzeros of the preconditioning matrix. By controlling the number of nonzeros, both the coefficient and the preconditioning matrices can fit on the cache memory. By using the cache memory effectively, the computation time of the ICT-based subdomain local solver becomes comparable to that of the direct LDL-based solver. In addition, when the number of degrees of freedom (DOFs) of an analysis model becomes very large, the LDL-based DDM solver suffers from overflow of the main memory whereas the ICT-based solver can complete the analysis. Using this solver, we succeeded in analyzing a structural problem of 64 million DOFs in 8 minutes on a parallel computing cluster of 8 nodes.
机译:参考文献(16)要通过域分解方法(DDM)分析大规模问题,加速子域局部求解器很重要。为了有效利用缓存并节省主内存使用量,我们使用了针对DDM的子域本地求解器优化的具有阈值(ICT)的不完整Cholesky分解预处理器。尽管ICT预处理器最初是针对病态问题而提出的,但我们可以在本研究中使用它,因为它可以自由地控制预处理矩阵的非零数。通过控制非零的数量,系数矩阵和预处理矩阵均可适合高速缓存。通过有效地使用高速缓存,基于ICT的子域本地求解器的计算时间变得与基于直接LDL的求解器的计算时间相当。另外,当分析模型的自由度(DOF)数量很大时,基于LDL的DDM求解器会遭受主内存溢出的困扰,而基于ICT的求解器则可以完成分析。使用此求解器,我们成功地在8个节点的并行计算集群上,在8分钟内分析了6400万个自由度的结构问题。

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