Modeling 1D Distributed-Memory Dense Kernels for an Asynchronous Multifrontal Sparse Solver

To solve sparse linear systems multifrontal methods rely on dense partial LU decompositions of so-called frontal matrices; we consider a parallel, asynchronous setting in which several frontal matrices can be factored simultaneously. In this context, to address performance and scalability issues of acyclic pipelined asynchronous factorization kernels, we study models to revisit properties of left and right-looking variants of partial LU decompositions, study the use of several levels of blocking, before focusing on communication issues. The general purpose sparse solver MUMPS has been modified to implement the proposed algorithms and confirm the properties demonstrated by the models.