The new preconditioner possesses much greater inherent parallelism than traditional preconditioners based on incomplete LU factorizations. Numerical experiments are used to show the effectiveness and efficiency of the new sparse approximate inverse preconditioner. We investigate the use of sparse approximate inverse techniques in a multi-level block ILU preconditioner to design a robust and efficient parallelizable preconditioner for solving general sparse matrices. Moreover, the new preconditioner affords maximum parallelism for operations within each level as well as for the coarsest level solution. Numerical experiments using several sets of test matrices arising from various realistic CFD applications are reported.
Multilevel methods are, in particular, a strong research thrust here.
Three S. Chowla Research Assistant Professors will be appointed beginning August 1999. Two are supported by the NSF and are therefore limited to US citizens and permanent residents. Chowla Assistant Professors are expected to be new or recent Ph.D.s with exceptional research potential and a commitment to education. The NSF-supported Chowla Professors will receive a summer research salary. The teaching load for all the positions is between two and three courses per year.