On the Successive Overrelaxation Method

The application of parallel computers to reservoir simulation is studied with the aid of two recently introduced Multiple Instruction, Multiple Data (MIMD) computers. The problems posed by reservoir simulators are shown to be particularly well suited for such parallel computers* Fundamental parallel programming techniques using Fortran are presented for the Hetrogeneous Element Processor (HEP) produced by Denelcor and the iPSC Hypercube produced by Intel. These techniques are then applied to two of the most time consuming tasks in reservoir simulation: forming the matrix coefficients and producing a sparse matrix solution. The problem of parallel formation of sparse matrix coefficients is addressed for the single and multiphase cases using both black oil and compositional fluid models. Parallel sparse matrix solution is addressed by considering D4 ordered Gaussian elimination, and the multigrid, conjugate gradient, and Successive Over-Relaxation (SOR) methods. Three parallel algorithms based on SOR are developed to illustrate the possible diversity of parallel algorithms. The red-black and multicolored SOR methods which are often used on vector machines are shown to be easily adaptable to parallel MIMD machine. Matrix partitioning and a new method of iteration pipelining are also presented as parallel SOR methods. The various SOR algorithms, in parallel form, are mapped onto the HEP to illustrate the speedups currently possible on an MIMD machine. Projections are made on the future importance of these types of machines to reservoir simulation.

In this research paper a new modification of Gauss-Seidel method has been presented for solving the system of linear algebraic equations. The systems of linear algebraic equations have an important role in the field of science and engineering. This modification has been developed by using the procedure of Gauss-Seidel method and the concept of substitution techniques. Developed modification of Gauss-Seidel method is a fast convergent as compared to Gauss Jacobi’s method, Gauss-Seidel method and successive over-relaxation (SOR) method. It works on the diagonally dominant as well as positive definite symmetric systems of linear algebraic equations. Its solution has been compared with the Gauss Jacobi’s method, Gauss-Seidel method and Successive over-Relaxation method by taking different systems of linear algebraic equations and found that, it was reducing to the number of iterations and errors in each problem.

This study presents an efficacy comparison of iterative solution methods for solving the compressible-fluid Reynolds equation in modeling air- or gas-lubricated bearings. A direct fixed-point iterative (DFI) method and Newton’s method are employed to transform the Reynolds equation in a form that can be solved iteratively. The iterative solution methods examined are the Gauss–Seidel method, the successive over-relaxation (SOR) method, the preconditioned conjugate gradient (PCG) method, and the multigrid method. The overall solution time is affected by both the transformation method and the iterative method applied. In this study, Newton’s method shows its effectiveness over the straightforward DFI method when the same iterative method is used. It is demonstrated that the use of an optimal relaxation factor is of vital importance for the efficiency of the SOR method. The multigrid method is an order faster than the PCG and optimal SOR methods. Also, the multigrid and PCG methods involve an extended coding work and are less flexible in dealing with gridwork and boundary conditions. Consequently, a compromise has to be made in terms of ease of use as well as programming effort for the solution of the compressible-fluid Reynolds equation.

PC cluster systems are becoming popular in the field of high-performance computing. The authors have been studying the electromagnetic field analysis of high T/sub c/ superconductors (HTS) by the finite element method (FEM) for AC loss estimations. Superconductors are highly nonlinear electromagnetic media, and considerable computation time is required to calculate the temporal evolution of the electromagnetic field distribution in superconductors. In this study, parallel computing techniques were applied to the electromagnetic field analysis of HTSs by the finite element method. In one of the FEM codes used by the authors, the successive over-relaxation (SOR) method, was used to solve a system of equations. This part was parallelized using the multicolor SOR method, and it helped in reducing computation time. Data are explicitly passed between the processor elements (PEs) through an MPI. First, the HTS model was analyzed under one operating condition, using the code implemented multicolor SOR method, using four PEs, and the parallelization efficiency was confirmed. Next, the generality of the efficiency of the multicolor SOR method in our numerical simulation was examined under several operating conditions, using different models.

A fully vectorizable SOR variant

Recently, special attention has been given in the literature, to the problems of accurately computing the least-squares solution of very largescale over-determined systems of linear equations which occur in geodetic applications. In particular, it has been suggested that one can solve such problems iteratively by applying the block SOR (Successive Overrelaxation) and EGS1 (Extrapolated Gauss Seidel 1) plus semi-iterative methods to a linear system with coefficient matrix 2-cyclic or 3-cyclic. The comparison of 2-block SOR and 3-block SOR was made in [1] and showed that the 2-block SOR is better. In [6], the authors also proved that 3-block EGS1-SI is better than 3-block SOR. Here, we first show that the 2-block DJ (Double Jacobi)-SI, GS-SI and EGS1-SI methods are equivalent and all of them are equivalent to the 3-block EGS1-SI method; then, we prove that the composite methods and 2-block SOR have the same asymptotic rate of convergence, but the former has a better average rate of convergence; finally, numerical experiments are reported, and confirm that the 3-block EGS1-SI is better than the 2-block SOR.

Projected successive overrelaxation method for finite-element solutions to the Dirichlet problem for a system of nonlinear elliptic equations

Chapter 15 - SYMMETRIC SOR METHOD AND RELATED METHODS

The Successive Over Relaxation (SOR) is a variant of the iterative Gauss-Seidel method for solving a linear system of equations Ax=b. The SOR algorithm is used within the NEMO (Nucleus for European Modelling of the Ocean) ocean model for solving the elliptical equation for the barotropic stream function. The NEMO performance analysis shows that the SOR algorithm introduces a significant communication overhead. Its parallel implementation is based on the Red-Black method and foresees a communication step at each iteration. An enhanced parallel version of the algorithm has been developed by acting on the size of the overlap region to reduce the frequency of communications. The overlap size must be carefully tuned for reducing the communication overhead without increasing the computing time. This work describes an analytical performance model of the SOR algorithm that can be used for establishing the optimal size of the overlap region.

The successive over-relaxation (SOR) iteration method for solving linear systems of equations depends upon a relaxation parameter. A well-known theory for determining this parameter was given by Young for consistently ordered matrices. In this paper, for the three-dimensional Laplacian, we introduce several compact difference schemes and analyse the block-SOR method for the resulting linear systems. Their optimum relaxation parameters are given for the first time. Analysis shows that the value of the optimum relaxation parameter of block-SOR iteration is very sensitive for compact stencils when solving the three-dimensional Laplacian. This paper provides a theoretical solution for determining the optimum relaxation parameter in real applications. doi:10.1017/S1446181109000261

In this paper we consider various iterative methods for solving systems of linear algebraic equations. We shall be primarily concerned with large systems with sparse matrices such as arise in the solution of elliptic boundary value problems in two dimensions by finite difference methods. Our discussion is divided into two parts: First, we review the well-known facts about such methods as the Jacobi, Gauss-Seidel, and successive overrelaxation methods. A treatment of various acceleration techniques is included. In the second part of the paper we consider the symmetric overrelaxation method and describe some practical procedures which can be used to obtain very rapid convergence. By showing that the method can be very effective in many cases and by outlining a definite procedure for its application, we hope to encourage its wider usage and to stimulate further research.

A three-dimensional finite difference computer simulation has been used to model an entire spherical volume conductor. The model permits conductivity to be dependent on distance from the center of the sphere. The method of successive overrelaxation is employed to solve the finite difference equations in a spherical nonhomogeneous, nonequidistant net. A new method of using successive overrelaxation along each space direction is presented. This method is very suitable for giving starting potentials of the normal successive overrelaxation.

In this paper, a novel iterative detection technique that combines deep learning (DL) and the approximated algorithm of successive over relaxation (SOR) is proposed to achieve high reliability and reduce the computational complexity. Recently, as the demanded data rates increase, the massive multiple-input and multiple-output (MIMO) system has drawn attention in wireless communication. In massive MIMO, the implementation of traditional detectors for high reliability has become impractical, and the reduction for the complexity of detectors has emerged as a practical implementation challenge. The existing DL-based detection technique of orthogonal approximate message passing network (OAMPNet) can provide high detection performance. However, the computational complexity is too high for the implementation in massive MIMO systems. The proposed detection technique uses SOR algorithm to reduce the computational complexity, and the relaxation parameter of SOR is adaptively determined by a learning algorithm. A non-linear estimator using the DL algorithm is combined with the SOR algorithm to achieve high reliability, and regardless of the size of the MIMO system, only the size of the DL architecture determines the complexity of the non-linear estimator. Simulation results show that the proposed detector outperforms the conventional linear detector based on minimum mean square error (MMSE) and achieves high reliability with lower complexity than OAMPNet in various channel environments with spatial correlation.

A variable preconditioned GCR( [formula omitted]) method using the GSOR method for singular and rectangular linear systems

A practical asymptotical optimal SOR method