Sparse approximate inverse and multilevel block ILU preconditioning techniques for general sparse matrices

We investigate the use of sparse approximate inverse techniques in a multilevel block ILU preconditioner to design a robust and efficient parallelizable preconditioner for solving general sparse matrices. The resulting preconditioner retains robustness of the multilevel block ILU preconditioner (BILUM) and offers a convenient means to control the fill-in elements when large size blocks (subdomains) are used to form block independent set. Moreover, the new implementation of BILUM with a sparse approximate inverse strategy affords maximum parallelism for operations within each level as well as for the coarsest level solution. Thus it has two advantages over the standard BILUM preconditioner: the ability to control sparsity and increased parallelism. Numerical experiments are used to show the effectiveness and efficiency of the proposed variant of BILUM.