The Nonsingularity of Sparse Approximate Inverse Preconditioning and Its Performance Based on Processor Virtualization
PPL Technical Report 2005
Publication Type: Paper
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Abstract
In this paper, we analyze the properties of the sparse approximate
inverse preconditioner, and prove that for a strictly diagonally
dominant M matrix, the computed preconditioning matrix can be
guaranteed to be nonsingular if it is nonnegative. Then we
investigate the use of the processor virtualization technique to
parallelize the sparse approximate inverse solver. Numerical
experiments on a distributed memory parallel computer show that the
efficiency of the resulting preconditioner can be improved by
virtualization.
TextRef
Kai Wang Orion Lawlor Laxmikant V. Kale, "The Nonsingularity of Sparse
Approximate Inverse Preconditioning and Its Performance Based on Processor
Virtualization", Parallel Programming Laboratory, Department of Computer Science,
University of Illinois at Urbana-Champaign, 2005.
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