A new parallel sparse approximation inverse preconditioner in MLFMA

We investigate a new parallel preconditioning technique in the iterative solution of large sparse linear system arising from electromagnetic scattering simulation involving three-dimensional conducting bodies with arbitrary shapes. Previous studies are focused on using static or matrix pattern based incomplete LU (ILU) preconditioners in a localized preconditioner implementation. Our current studies is concerned about preconditioner performance for solving large problems with tens of millions of unknowns, we use a global sparse approximate inverse preconditioner with a static sparsity pattern to achieve robustness and efficiency.