Solutions of large-scale electromagnetics problems involving dielectric objects with the parallel multilevel fast multipole algorithm

O. Ergul

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29 Citations (Scopus)
221 Downloads (Pure)

Abstract

Fast and accurate solutions of large-scale electromagnetics problems involving homogeneous dielectric objects are considered. Problems are formulated with the electric and magnetic current combined-field integral equation and discretized with the Rao-Wilton-Glisson functions. Solutions are performed iteratively by using the multi-level fast multipole algorithm (MLFMA). For the solution of large-scale problems discretized with millions of unknowns, MLFMA is parallelized on distributed-memory architectures using a rigorous technique, namely, the hierarchical partitioning strategy. Efficiency and accuracy of the developed implementation are demonstrated on very large problems involving as many as 100 million unknowns.
Original languageEnglish
Pages (from-to)2261-2268
Number of pages8
JournalJournal of the Optical Society of America A
Volume28
Issue number11
DOIs
Publication statusPublished - Nov 2011

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multipoles
electromagnetism
distributed memory
Memory architecture
electric current
Integral equations
integral equations

Keywords

  • strategy
  • scattering
  • MLFMA
  • unknowns
  • millions
  • field integral-equation
  • solutions
  • large-scale
  • electromagnetics problems
  • dielectric objects
  • parallel
  • multilevel fast multipole algorithm

Cite this

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AB - Fast and accurate solutions of large-scale electromagnetics problems involving homogeneous dielectric objects are considered. Problems are formulated with the electric and magnetic current combined-field integral equation and discretized with the Rao-Wilton-Glisson functions. Solutions are performed iteratively by using the multi-level fast multipole algorithm (MLFMA). For the solution of large-scale problems discretized with millions of unknowns, MLFMA is parallelized on distributed-memory architectures using a rigorous technique, namely, the hierarchical partitioning strategy. Efficiency and accuracy of the developed implementation are demonstrated on very large problems involving as many as 100 million unknowns.

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