Parallelization methods for implementation of a magnetic induction tomography forward model in symmetric multiprocessor systems

Authors:
Yasheng Maimaitijiang;Mohammed Ali Roula;Stuart Watson;Ralf Patz;Robert J. Williams;Huw Griffiths
Affiliations:
Faculty of Advanced Technology, University of Glamorgan, Pontypridd CF37 1DL, UK;Faculty of Advanced Technology, University of Glamorgan, Pontypridd CF37 1DL, UK;Faculty of Advanced Technology, University of Glamorgan, Pontypridd CF37 1DL, UK;Faculty of Advanced Technology, University of Glamorgan, Pontypridd CF37 1DL, UK;Faculty of Advanced Technology, University of Glamorgan, Pontypridd CF37 1DL, UK;Medical Physics and Clinical Engineering, Singleton Hospital, Swansea SA2 8QA, UK
Venue:
Parallel Computing
Year:
2008

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Abstract

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.