• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.32 no.2
• /
• pp.117-124
• /
• 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 Chungcheong Mathematical Society
• /
• v.10 no.1
• /
• pp.147-159
• /
• 1997

It is important to solve the large sparse linear system appeared in many application field such as $AA^Ty={\beta}$ efficiently. In solving this linear system, the sparse solver using the splitting method for the relatively dense column is experimentally better than the direct solver using the Cholesky method.

• Computational Structural Engineering
• /
• v.19 no.4 s.74
• /
• pp.37-43
• /
• 2006

• Computational Structural Engineering
• /
• v.4 no.2
• /
• pp.103-110
• /
• 1991

To solve the large problems with limited core memory of computer, a disk management scheme called virtual memory database has been developed. Utilizing this technique along with memory moving scheme, an efficient in-and out-of-core column solver for the sparse symmetric matrix commonly arising in the finite element analysis is developed. Compared with other methods the algorithm is simple, therefore the coding and computational efficiencies are greatly enhanced. Analysis example shows that the proposed method efficiently solve the large structural problem on the small-memory micro-computer.

• Geophysics and Geophysical Exploration
• /
• v.7 no.4
• /
• pp.234-244
• /
• 2004

In order to calculate 3-dimensional wavefield using finite-element method in frequency domain, we must factor so huge sparse impedance matrix. Because of difficulties of handling of this huge impedance matrix, 3-dimensional wave equation modeling is conducted mainly in time domain. In this study, we simulate the 3-D wavefield using finite-element method in Laplace domain by combining high-performance parallel finite-element solver and SWEET (Suppressed Wave Equation Estimation of Traveltime) algorithm which can calculate the traveltime and the amplitude. To verify this combination, we applied it to the SEG/EAGE 3D salt model in serial and parallel computing environments.

• The KIPS Transactions:PartA
• /
• v.8A no.3
• /
• pp.227-234
• /
• 2001

In this paper we propose a block-type parallel preconditioner for solving large sparse nonsymmetric linear systems, which we expect to be scalable. It is Multi-Color Block SOR preconditioner, combined with direct sparse matrix solver. For the Laplacian matrix the SOR method is known to have a nondeteriorating rate of convergence when used with Multi-Color ordering. Since most of the time is spent on the diagonal inversion, which is done on each processor, we expect it to be a good scalable preconditioner. We compared it with four other preconditioners, which are ILU(0)-wavefront ordering, ILU(0)-Multi-Color ordering, SPAI(SParse Approximate Inverse), and SSOR preconditiner. Experiments were conducted for the Finite Difference discretizations of two problems with various meshsizes varying up to $1025{\times}1024$. CRAY-T3E with 128 nodes was used. MPI library was used for interprocess communications, The results show that Multi-Color Block SOR is scalabl and gives the best performances.

• Journal of the Korea Society of Computer and Information
• /
• v.26 no.1
• /
• pp.1-9
• /
• 2021

OpenGL compute shader is a shader stage that operate differently from other shader stage and it can be used for the calculating purpose of any data in parallel. This paper proposes a GPU-based parallel algorithm for computing sparse linear systems through conjugate gradient using an iterative method, which perform calculation on OpenGL compute shader. Basically, this sparse linear solver is used to solve large linear systems such as symmetric positive definite matrix. Four well-known matrix formats (Dense, COO, ELL and CSR) have been used for matrix storage. The performance comparison from our experimental tests using eight sparse matrices shows that GPU-based linear solving system much faster than CPU-based linear solving system with the best average computing time 0.64ms in GPU-based and 15.37ms in CPU-based.

• 한국전산유체공학회:학술대회논문집
• /
• 1998.11a
• /
• pp.153-159
• /
• 1998

The Newton-Krylov method on the unstructured grid flow solver using the cell-centered spatial discretization oi compressible Euler equations is presented. This flow solver uses the reconstructed primitive variables to get the higher order solutions. To get the quadratic convergence of Newton method with this solver, the careful linearization of face flux is performed with the reconstructed flow variables. The GMRES method is used to solve large sparse matrix and to improve the performance ILU preconditioner is adopted and vectorized with level scheduling algorithm. To get the quadratic convergence with the higher order schemes and to reduce the memory storage. the matrix-free implementation and Barth's matrix-vector method are implemented and compared with the traditional matrix-vector method. The convergence and computing times are compared with each other.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 1990.10a
• /
• pp.34-39
• /
• 1990

For the analysis of structures, specifically if it is large-scale, in which case it can not be solved within the core memory, the majority of computation time is consumed In the solution of simultaneous linear equation. In this study an efficient in- and out-of-core column solver for sparse symmetric matrix utilizing memory moving scheme is developed. Compare with existing blocking methods the algorithm is simple, therefore the coding and computational efficiencies are greatly enhanced. Upon available memory size, the solver automatically performs solution within the core or outside core. Analysis example shows that the proposed method efficiently solve the large structural problem on the small-memory microcomputer.

• Journal of the Korean Society for Industrial and Applied Mathematics
• /
• v.5 no.1
• /
• pp.85-100
• /
• 2001

In this paper we propose a block-type parallel preconditioner for solving large sparse nonsymmetric linear systems, which we expect to be scalable. It is Multi-Color Block SOR preconditioner, combined with direct sparse matrix solver. For the Laplacian matrix the SOR method is known to have a nondeteriorating rate of convergence when used with Multi-Color ordering. Since most of the time is spent on the diagonal inversion, which is done on each processor, we expect it to be a good scalable preconditioner. Finally, due to the blocking effect, it will be effective for ill-conditioned problems. We compared it with four other preconditioners, which are ILU(0)-wavefront ordering, ILU(0)-Multi-Color ordering, SPAI(SParse Approximate Inverse), and SSOR preconditioner. Experiments were conducted for the Finite Difference discretizations of two problems with various meshsizes varying up to 1024 x 1024, and for an ill-conditioned matrix from the shell problem from the Harwell-Boeing collection. CRAY-T3E with 128 nodes was used. MPI library was used for interprocess communications. The results show that Multi-Color Block SOR and ILU(0) with Multi-Color ordering give the best performances for the finite difference matrices and for the shell problem only the Multi-Color Block SOR converges.