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Local block factorization and its parallelization to block tridiagonal matrices

机译:局部块分解及其对块三对角矩阵的并行化

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A type of incomplete decomposition preconditioner based on local block factorization is considered, for the matrices derived from discreting 2-D or 3-D elliptic partial differential equations. We prove that the condition numbers of the preconditioned matrices are small, which means that the constructed preconditioners are effective. Further we consider an efficient parallel version of the preconditioner which depends only on a single integer argument. When its value is small, the iterations needed on multiple processors to converge is much more than on a single processor But with the increase of this value, the difference decreases step by step. Finally, we have many experiments on a cluster of 6 PCs with main frequencies of 1.8GHz. The results show that the local block factorization constructed are efficient in serial implementation, if compared to some well-known effective preconditioners, and the parallel versions are also efficient.
机译:对于离散2D或3D椭圆偏微分方程的矩阵,考虑了一种基于局部块分解的不完全分解预处理器。我们证明了预处理矩阵的条件数很小,这意味着构造的预处理器是有效的。此外,我们考虑了预处理器的高效并行版本,该并行版本仅依赖于单个整数参数。当它的值小时,多个处理器收敛所需的迭代要比单个处理器要多得多,但是随着该值的增加,差异会逐步减小。最后,我们在6个PC的群集上进行了许多实验,这些PC的主频率为1.8GHz。结果表明,与某些众所周知的有效预处理器相比,构造的局部块分解在串行实现中是有效的,而并行版本也是有效的。

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