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. Through a few numerical experiments, we conduct a comparable study on the properties and performance of the SAI preconditioners using the different sparsity patterns for solving some sparse linear systems.
|Number of pages||13|
|Journal||Journal of Mathematical Modelling and Algorithms|
|State||Published - 2003|
Bibliographical noteFunding Information:
★ This research was supported in part by the U.S. National Science Foundation under grants CCR-9902022, CCR-9988165, CCR-0092532, and ACI-0202934, in part by the Japan Research Organization for Information Science & Technology, and in part by the University of Kentucky Research Committee.
- Dynamic and static sparsity pattern
- Parallel preconditioning
- Sparse approximate inverse
- Sparse matrices
ASJC Scopus subject areas
- Modeling and Simulation
- Applied Mathematics