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首页> 外文期刊>SIAM Journal on Scientific Computing >A parallelization technique B ASED on factor combination and graph partitioning for general incomplete LU factorization
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A parallelization technique B ASED on factor combination and graph partitioning for general incomplete LU factorization

机译:基于因子组合和图划分的并行化技术用于一般不完全LU分解

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We present a new parallelization scheme based on factor combination for general incomplete LU factorization. In this scheme, overlapped domain decomposition based on adjacent graphs is applied, and a sequence of overlapped subgraphs is formed. For each subgraph, any kind of incomplete LU factorization can be applied. The overall parallel preconditioner is then constructed as the product of the overall upper and lower triangular factors, which are derived from the combination of local factors with the idea of restricted additive Schwarz. In the solution of auxiliary linear systems related to the preconditioner, the overall factors are formed implicitly to reduce the computational cost. The analyses show that the new scheme is more effective than classical additive Schwarz and restricted additive Schwarz. When local preconditioners are symmetric positive definite, the derived parallel version preserves the property, which is vital to the conjugate gradient iterations. Finally, the new technique is tested in solving linear systems from the model two-dimensional (2D) and three-dimensional (3D) partial differential equations with finite differences and those from mesoscale numerical simulation of concrete. The results show that it is usually superior to classical additive Schwarz and block Jacobi. For nonsymmetric cases, it is also comparable to restricted additive Schwarz.
机译:我们提出了一种基于因子组合的通用并行化方案,用于一般不完全LU分解。在该方案中,应用了基于相邻图的重叠域分解,并形成了一系列重叠子图。对于每个子图,可以应用任何类型的不完全LU分解。然后,将整体并行预处理器构造为整体上三角形和下三角形因子的乘积,这些三角形因子是局部因子与受限加性Schwarz思想的结合而得出的。在与预处理器相关的辅助线性系统的解决方案中,隐式形成了总体因素以降低计算成本。分析表明,该新方案比传统的添加剂Schwarz和受限的添加剂Schwarz更有效。当局部前置条件是对称正定的时,派生的并行版本会保留该属性,这对于共轭梯度迭代至关重要。最后,在具有有限差分的二维(2D)和三维(3D)偏微分方程模型以及混凝土的中尺度数值模拟中求解线性系统时,对新技术进行了测试。结果表明,它通常优于经典添加剂Schwarz和Block Jacobi。对于非对称情况,它也可以与受限添加剂Schwarz相提并论。

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