Scalable parallelization of the sparse-approximate-inverse (SAl) preconditioner for the solution of large-scale integral-equation problems

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Scalable parallelization of the sparse-approximate-inverse (SAl) preconditioner for the solution of large-scale integral-equation problems.pdf (1.09 MB)

Date

2009-06

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IEEE Antennas and Propagation Society, AP-S International Symposium (Digest)

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1522-3965

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IEEE

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1 - 4

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English

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Conference Paper

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Abstract

In this paper, we consider efficient parallelization of the sparse approximate inverse (SAI) preconditioner in the context of the multilevel fast multipole algorithm (MLFMA). Then, we report the use of SAI in the solution of very large EFIE problems. The SAI preconditioner is important not only because it is a robust preconditioner that renders many difficult and large problems solvable, but also it can be utilized for the construction of more effective preconditioners.

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Large-scale systems , Costs , Ear , Robustness , MLFMA , Partitioning algorithms , Computational electromagnetics , Electromagnetic scattering , Integral equations , Concurrent computing

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http://hdl.handle.net/11693/28690

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http://dx.doi.org/10.1109/APS.2009.5172284

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Department of Electrical and Electronics Engineering
Computational Electromagnetics Research Center (BiLCEM)