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)

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2009-06

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

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IEEE

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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

Published Version (Please cite this version)

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

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Scholarly Publications - Electrical and Electronics Engineering
Scholarly Publications - BİLCEM

Language

English

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Conference Paper