Sparse approximate inverse (SAI) techniques have recently emerged as a new class  of parallel preconditioning techniques for solving large sparse linear systems on high  performance computers. The choice of the sparsity pattern of the SAI matrix is probably  the most important step in constructing an SAI preconditioner. Both dynamic  and static sparsity pattern selection approaches have been proposed by researchers