Performance of ILU preconditioning techniques in simulating anisotropic diffusion in the human brain

Authors:
Ning Kang;Jun Zhang;Eric S. Carlson
Affiliations:
Laboratory for High Performance Scientific Computing and Computer Simulation, Department of Computer Science, University of Kentucky, Lexington, KY;Laboratory for High Performance Scientific Computing and Computer Simulation, Department of Computer Science, University of Kentucky, Lexington, KY;Department of Chemical Engineering, University of Alabama, P.O. Box 870203, Tuscaloosa, AL
Venue:
Future Generation Computer Systems - Special issue: Advanced services for clusters and internet computing
Year:
2004

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Abstract

We conduct simulations for the unsteady state anisotropic diffusion process in the human brain by discretizing the governing diffusion equation on a face-centered cubic grid and adopting a high performance differential-algebraic equation solver, IDA, to deal with the resulting large-scale system of DAEs. Incomplete LU preconditioning techniques are used with the GMRES method to accelerate the convergence rate of the iterative solution. We then investigate and compare the efficiency and effectiveness of a number of ILU preconditioners, and find out that the ILUT with a dual dropping strategy gives the best overall performance when it is provided with the optimum choices of the fill-in parameter and the threshold dropping tolerance.