• Stuart Watson
  • Mohammed Roula
  • Ralf Patz
  • Y. Maimaitijiang
  • R.J. Williams
  • H. Griffiths
This paper describes four parallelization approaches used in a finite-difference-based electromagnetic modeller for application in magnetic induction tomography (MIT) and suitable for implementation on computer systems with symmetric multiprocessor (SMP) architecture. The approaches include: (i) splitting by coils using a distributed memory approach, (ii) splitting by physical domain using a distributed memory approach, (iii) splitting by physical domain using hybrid distributed/shared memory approach and (iv) splitting by both coils and physical domain using multi-level distributed and shared memory approaches respectively. All four approaches were implemented and tested on an IBM SP supercomputer. Coil parallelization was the most efficient method due to low inter-processor communication requirements but was limited by the number of coils in the MIT system. Approaches (ii) and (iii) allowed a larger number of processors to be employed but the efficiency versus number of processors was found to drop at a faster rate in comparison to (i). The fourth approach both allowed a larger number of processors to be employed and was found to provide higher efficiency than the parallelization by physical domain only. This multi-level hybrid approach therefore appears to offer an effective parallelization method for implementation of the MIT forward model on SMP clusters.
Original languageEnglish
Pages (from-to)497 - 507
Number of pages10
JournalJournal of Parallel Computing
Volume34
Issue number9
DOIs
Publication statusPublished - 1 Sep 2008

    Research areas

  • Magnetic induction tomography, SMP cluster, finite-difference algorithm, Red–black SOR

ID: 75228