SC Seminar: Raju Ram

Raju Ram, Fraunhofer ITWM

Parallel Multilevel ILU Preconditioner to Solve Large Linear System of Equations


The solution of large sparse linear systems is a ubiquitous problem in chemistry, physics, and engineering applications. Krylov subspace based iterative methods are preferred to solve the linear system instead of direct methods as they are faster and use less memory. These methods use preconditioners to accelerate the convergence of underlying iterative methods.

Parallel performance of iterative methods is largely determined by the scalability of the underlying iterative solver and its preconditioner. In particular, scalability of the preconditioner is the most challenging operation. For example, the incomplete LU decomposition (ILU) preconditioner algorithm is serial in nature. One should modify the serial ILU algorithm to extract the parallelism.

New approaches, where the kernels of the ILU preconditioners are split up into fine-grained task-based implementations to deal with intra-node concurrency are promising. In this talk, we present parallel multilevel ILU preconditioner. Our future goals are shared memory implementation of the ILU preconditioner in linear solver library GaspiLS, followed by GASPI based distributed memory implementation.

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