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Hybridizing Nested Dissection and Halo Approximate Minimum Degree for Efficient Sparse matrix Ordering

机译:混合嵌套解剖与Halo近似最小度的有效稀疏矩阵排序

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Minimum Degree and Nested Disssection re the two most popular reordering schemes used ot reduce fill-in and operation count when factoring and solving sparse matrices. Most of the state-of-the-art ordering packages hybridize these methods by prforming incomplete Nested Dissection and ordering by Minimum Degree the subgraphs associated with the leaves of the separatio tree, but to date only loose couplings have been achieved, resulting in poorer performance than could have been expected. This paper presents a tight coupling of hte Nested dissection and Halo Approximate Minimum Degree algorithms, which allows the minimum degree algorithm to use exact degrees on the boundaries of the subgraphs passed to it, and to yield back not only the ordering of the nodes of the subgraph, but also the amalgamated assembly subtrees, for efficient block computations. Experimental results show the performance improvement, both in terms of fill-in reduction and concurrency during numerical factorization.
机译:最小度和嵌套解剖是用于分解和求解稀疏矩阵时减少填充和运算次数的两种最流行的重排序方案。大多数最先进的排序软件包通过执行不完整的嵌套解剖和按最小程度对与分离树的叶子相关的子图进行排序来混合这些方法,但是迄今为止,仅实现了松散耦合,从而导致性能降低超出了预期。本文提出了嵌套剖析和Halo近似最小度算法的紧密结合,它允许最小度算法在传递给它的子图的边界上使用精确度,并且不仅返回节点的顺序。子图,以及合并的装配子树,以进行有效的块计算。实验结果表明,在数值分解期间的填充减少和并发性方面,性能都有改进。

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