Scaling and Pivoting in an Out-of-Core Sparse Direct Solver

Out-of-core sparse direct solvers reduce the amount of main memory needed to factorize and solve large sparse linear systems of equations by holding the matrix data, the computed factors, and some of the work arrays in files on disk. The efficiency of the factorization and solution phases is dependent upon the number of entries in the factors. For a given pivot sequence, the level of fill in the factors beyond that predicted on the basis of the sparsity pattern alone depends on the number of pivots that are delayed (i.e., the number of pivots that are used later than expected because of numerical stability considerations). Our aim is to limit the number of delayed pivots, while maintaining robustness and accuracy. In this article, we consider a new out-of-core multifrontal solver HSL MA78 from the HSL mathematical software library that is designed to solve the unsymmetric sparse linear systems that arise from finite element applications. We consider how equilibration can be built into the solver without requiring the system matrix to be held in main memory. We also examine the effects of different pivoting strategies, including threshold partial pivoting, threshold rook pivoting, and static pivoting. Numerical experiments on problems arising from a range of practical applications illustrate the importance of scaling and show that, in some cases, rook pivoting can be more efficient than partial pivoting in terms of both the factorizatio