Abstract
It has been well-known that ILU(0) factorization is very effective preconditioning method when large-scale linear sparse systems in scientific and engineering computations are solved iteratively. But it's also well-known that this method requires global data dependency and this is not the optimal way on parallel computers where locality is of utmost importance. In this paper, ”Localized” ILU(0) preconditioning method has been implemented to various type of iterative solvers. In this method. ILU(0) factorization is carried out for each processor by zeroing out the matrix components whose column numbers are outside the processor domain. This method provides data locality on each processor and good parallelization effect. Developed system performance has been also evaluated on simulated parallel processors by workstation cluster with PVM.KeywordsUnstructured GridKrylov SubspaceIterative SolverDomain NumberParallel Virtual MachineThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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