Spectral Nested Dissection 1

We describe a spectral nested dissection algorithm for computing orderings appropriate for parallel factorization of sparse, symmetric matrices. The algorithm makes use of spectral properties of the Laplacian matrix associated with the given matrix to compute separators. We evaluate the quality of the spectral orderings with respect to several measures: fill, elimination tree height, height and weight balances of elimination trees, and clique tree heights. Spectral orderings compare quite favorably with commonly used orderings, outperforming them by a wide margin for some of these measures. These results are confirmed by computing a multifrontal numerical factorization with the different orderings on a Cray Y-MP with eight processors. Abstract. We describe a spectral nested dissection algorithm for computing orderings appropriate for parallel factorization of sparse, symmetric matrices. The algorithm makes use of spectral properties of the Laplacian matrix associated with the given matrix to compute separators. We evaluate the quality of the spectral orderings with respect to several measures: fill, elimination tree height, height and weight balances of elimination trees, and clique tree heights. Spectral orderings compare quite favorably with commonly used orderings, outperforming them by a wide margin for some of these measures. These results are confirmed by computing a multifrontal numerical factorization with the different orderings on a Cray Y-MP with eight processors.

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