• Journal of the Computational Structural Engineering Institute of Korea
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• v.32 no.2
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• pp.117-124
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• 2019

Parallel sparse solvers are essential for solving large-scale finite element models. This paper introduces the combination of iterative and direct solver that can be applied efficiently to problems that require continuous solution for a subtly changing sequence of systems of equations. The iterative-direct sparse solver combination technique, proposed and implemented in the parallel sparse solver package, PARDISO, means that iterative sparse solver is applied for the newly updated linear system, but it uses the direct sparse solver's factorization of previous system matrix as a preconditioner. If the solution does not converge until the preset iterations, the solution will be sought by the direct sparse solver, and the last factorization results will be used as a preconditioner for subsequent updated system of equations. In this study, an improved method that sets the maximum number of iterations dynamically at the first Krylov iteration step is proposed and verified thereby enhancing calculation efficiency by the frequency domain analysis.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.30 no.2
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• pp.127-135
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• 2017

The computing environment has changed rapidly to enable large-scale finite element models to be analyzed at the PC or workstation level, such as multi-core CPU, optimal math kernel library implementing BLAS and LAPACK, and popularization of direct sparse solvers. In this paper, the design considerations on a parallel finite element code for shared memory based multi-core CPU system are proposed; (1) the use of optimized numerical libraries, (2) the use of latest direct sparse solvers, (3) parallelism using OpenMP for computing element stiffness matrices, and (4) assembly techniques using triplets, which is a type of sparse matrix storage. In addition, the parallelization effect is examined on the time-consuming works through a large scale finite element model.