A Newton two-stage waveform relaxation method for solving systems of nonlinear algebraic equations

In this paper, a Newton two-stage waveform relaxation method is introduced to solve systems of nonlinear algebraic equations. The proposed method is derived from the Newton waveform relaxation method by adding further splitting function and inner iterations. Sufficient conditions for the convergence of the method have been provided. Some numerical examples are given to show the effectiveness of the presented method and to compare with two available methods

Waveform Relaxation for the Computational Homogenization of Multiscale Magnetoquasistatic Problems

This paper proposes the application of the waveform relaxation method to the homogenization of multiscale magnetoquasistatic problems. In the monolithic heterogeneous multiscale method, the nonlinear macroscale problem is solved using the Newton--Raphson scheme. The resolution of many mesoscale problems per Gauss point allows to compute the homogenized constitutive law and its derivative by finite differences. In the proposed approach, the macroscale problem and the mesoscale problems are weakly coupled and solved separately using the finite element method on time intervals for several waveform relaxation iterations. The exchange of information between both problems is still carried out using the heterogeneous multiscale method. However, the partial derivatives can now be evaluated exactly by solving only one mesoscale problem per Gauss point.Comment: submitted to JC

Transient Analysis of High-Speed Channels via Newton-GMRES Waveform Relaxation

This paper presents a technique for the numerical simulation of coupled high-speed channels terminated by arbitrary nonlinear drivers and receivers. The method builds on a number of existing techniques. A Delayed-Rational Macromodel is used to describe the channel in compact form, and a general Waveform Relaxation framework is used to cast the solution as an iterative process that refines initial estimates of transient scattering waves at the channel ports. Since a plain Waveform Relaxation approach is not able to guarantee convergence, we turn to a more general class of nonlinear algebraic solvers based on a combination of the Newton method with a Generalized Minimal Residual iteration, where the Waveform Relaxation equations act as a preconditioner. The convergence of this scheme can be proved in the general case. Numerical examples show that very few iterations are indeed required even for strongly nonlinear termination

Parallel numerical methods for large-scale DAE systems

For plantwide dynamic simulation in chemical process industry, parallel numerical methods using a divide and conquer strategy are considered. An approach for the numerical solution of initial value problems for large systems of differential algebraic equations (DAEs) arising from industrial applications and its realization on parallel computers with shared memory is discussed. The system is partitioned into blocks and then it is extended appropriately, such that block-structured Newton-type methods can be applied which enable the application of relaxation techniques. This approach has gained considerable speedup factors for the dynamic simulation of various large-scale distillation plants, covering systems with up to 60 000 equations

The Parallel Implementation of the Waveform Relaxation Method for Transient Stability Simulations

In this paper, the authors extend the results of their earlier paper on waveform relamtion (WR), which is a parallel algorithm for transient stability analysis. The WR algorithm is extended to a structure-preserving power system model in which the loads are retained. This results in a system of differential/ algebraic equations (DAEs). Power systems exhibit several unique dynamic properties which may be exploited in an advantageous manner by the WR algorithm. This leads to a greater computational efficiency than most other direct methods of simulation. This paper presents several theoretical results as well as computational results on parallel implementation

Transient Stability Simulation by Waveform Relaxation Methods

In this paper, a new methodology for power system dynamic response calculations is presented. The technique known as the waveform relaxation has been extensively used in transient analysis of VLSI circuits and it can take advantage of new architectures in computer systems such as parallel processors. The application in this paper is limited to swing equations of a large power system. Computational results are presented

Circuit simulation using distributed waveform relaxation techniques

Simulation plays an important role in the design of integrated circuits. Due to high costs and large delays involved in their fabrication, simulation is commonly used to verify functionality and to predict performance before fabrication. This thesis describes analysis, implementation and performance evaluation of a distributed memory parallel waveform relaxation technique for the electrical circuit simulation of MOS VLSI circuits. The waveform relaxation technique exhibits inherent parallelism due to the partitioning of a circuit into a number of sub-circuits. These subcircuits can be concurrently simulated on parallel processors. Different forms of parallelism in the direct method and the waveform relaxation technique are studied. An analysis of single queue and distributed queue approaches to implement parallel waveform relaxation on distributed memory machines is performed and their performance implications are studied. The distributed queue approach selected for exploiting the coarse grain parallelism across sub-circuits is described. Parallel waveform relaxation programs based on Gauss-Seidel and Gauss-Jacobi techniques are implemented using a network of eight Transputers. Static and dynamic load balancing strategies are studied. A dynamic load balancing algorithm is developed and implemented. Results of parallel implementation are analyzed to identify sources of bottlenecks. This thesis has demonstrated the applicability of a low cost distributed memory multi-computer system for simulation of MOS VLSI circuits. Speed-up measurements prove that a five times improvement in the speed of calculations can be achieved using a full window parallel Gauss-Jacobi waveform relaxation algorithm. Analysis of overheads shows that load imbalance is the major source of overhead and that the fraction of the computation which must be performed sequentially is very low. Communication overhead depends on the nature of the parallel architecture and the design of communication mechanisms. The run-time environment (parallel processing framework) developed in this research exploits features of the Transputer architecture to reduce the effect of the communication overhead by effectively overlapping computation with communications, and running communications processes at a higher priority. This research will contribute to the development of low cost, high performance workstations for computer-aided design and analysis of VLSI circuits

Integration of continuous-time dynamics in a spiking neural network simulator

Contemporary modeling approaches to the dynamics of neural networks consider two main classes of models: biologically grounded spiking neurons and functionally inspired rate-based units. The unified simulation framework presented here supports the combination of the two for multi-scale modeling approaches, the quantitative validation of mean-field approaches by spiking network simulations, and an increase in reliability by usage of the same simulation code and the same network model specifications for both model classes. While most efficient spiking simulations rely on the communication of discrete events, rate models require time-continuous interactions between neurons. Exploiting the conceptual similarity to the inclusion of gap junctions in spiking network simulations, we arrive at a reference implementation of instantaneous and delayed interactions between rate-based models in a spiking network simulator. The separation of rate dynamics from the general connection and communication infrastructure ensures flexibility of the framework. We further demonstrate the broad applicability of the framework by considering various examples from the literature ranging from random networks to neural field models. The study provides the prerequisite for interactions between rate-based and spiking models in a joint simulation