Tradeoffs between parallelism and fill in nested dissection
In this paper we demonstrate that parallelism and fill can be traded off in orders for Gaussian elimination. While the well-known nested dissection algorithm produces very parallel elimination orders, we show that by reducing the parallelism it is possible to reduce the fill that the orders generate. In particular, we present a new `less parallel nested dissection' algorithm (LPND). We prove that, unlike standard nested dissection, when applied to a chordal graph LPND finds a zero-fill elimination order. Our implementation of LPND generates less fill than state-of-the-art implementations of the nested dissection (METIS), minimum-degree (AMD), and hybrid (BEND) algorithms on a large body of test matrices, at the cost of a small reduction in the paralellism in the orders that it produces. We have also implemented a nested dissection algorithm that is different from METIS and that uses the same separator algorithm used by our implementation of LPND. This algorithm, like LPND, generates less fill than METIS, and on large graphs generates significantly less fill than AMD. The latter comparison is notable, because although it is known that, for certain classes of graphs, minimum-degree produces asymptotically more fill than nested dissection, minimum-degree is believed to produce low-fill orderings in practice. Our experiments contradict this belief.
