A Scalable Sparse Direct Solver Using Static Pivoting

We propose several techniques as alternatives to partial pivoting to stabilize sparse Gaussian elimination. From numerical experiments we demonstrate that for a wide range of problems the new method is as stable as partial pivoting. The main advantage of the new method over partial pivoting is that it permits a priori determination of data structures and communication pattern, which makes it more scalable. We demonstrate the scalability of our algorithms on large-scale distributed memory computers.