Using postordering and static symbolic factorization for parallel sparse LU

In this paper we present several improvements of widely used parallel LU factorization methods on sparse matrices. First we introduce the LU elimination forest and then we characterize the L, U factors in terms of their corresponding LU elimination forest. This characterization can be used as a compact storage scheme of the matrix as well as of the task dependence graph. To improve the use of BLAS in the numerical factorization, we perform a postorder traversal of the LU elimination forest, thus obtaining larger supern-odes. To expose more task parallelism for a sparse matrix, we build a more accurate task dependence graph that in-cludes only the least necessary dependences. Experiments compared favorably our methods against methods implemented in the S * environment on the SGI’s Origin2000 multiprocessor. 1. Introduction an