The solution of large sparse linear systems is often the most time-consuming
part of many science and engineering applications. Computational fluid
dynamics, circuit simulation, power network analysis, and material science are
just a few examples of the application areas in which large sparse linear
systems need to be solved effectively. In this paper we introduce a new
parallel hybrid sparse linear system solver for distributed memory
architectures that contains both direct and iterative components. We show that
by using our solver one can alleviate the drawbacks of direct and iterative
solvers, achieving better scalability than with direct solvers and more
robustness than with classical preconditioned iterative solvers. Comparisons to
well-known direct and iterative solvers on a parallel architecture are
provided.Comment: To appear in Journal of Computational and Applied Mathematic