Highly parallel sparse triangular solution

Abstract

In this paper we survey a recent approach for solving sparse triangular systems of equations on highly parallel computers. This approach employs a partitioned representation of the inverse of the triangular matrix so that the solution can be computed by matrix-vector multiplication. The number of factors in the partitioned inverse is proportional to the number of general communication steps (router steps on a CM-2) required in a highly parallel algorithm. We describe partitioning algorithms that minimize the number of factors in the partitioned inverse over all symmetric permutations of the triangular matrix such that the permuted matrix continues to be triangular. For a Cholesky factor we describe an O(n) time and space algorithm to solve the partitioning problem above, where n is the order of the matrix. Our computational results on a CM-2 demonstrate the potential superiority of the partitioned inverse approach over the conventional substitution algorithm for highly parallel sparse triangular solution. Finally we describe current and future extensions of these results.