A Comparative Study on Dynamic and Static Sparsity Patterns in Parallel Sparse Approximate Inverse Preconditioning |
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Authors: | Kai Wang Sangbae Kim Jun Zhang |
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Institution: | (1) Laboratory for High Performance Scientific Computing and Computer Simulation, Department of Computer Science, University of Kentucky, Lexington, KY, 40506-0046, U.S.A |
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Abstract: | 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.
This revised version was published online in July 2006 with corrections to the Cover Date. |
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Keywords: | sparse matrices parallel preconditioning sparse approximate inverse dynamic and static sparsity pattern |
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