Performance Analysis of Parallel Virtual Machine in Solving Large-Scale Multi-dimensional Problems

Domain decomposition and expanded mixed method for parabolic partial differential equations

Chaos synchronization is a topic of great interest, due to its observation in a huge variety of phenomena of different nature. We study synchronization of two chaotic oscillators in a Master- Slave configuration. The two dynamic systems are coupled via a directed feedback that randomly switches among a finite set of given constant function at a prescribed time rate. And we use Lyapunov stability theory. This paper discussed the using of lag synchronization approach, and provided the equilibrium solutions of a new class of higher order parabolic partial differential equations to be applicable for Lorenz chaotic system in order to minimize the dynamical error of large Lorenz chaotic system

Some discrete Galerkin methods are formulated for approximately solving a linear fourth order parabolic partial differential equation in $\Omega \times (0,T]$, where $\Omega $ is an arbitrary bounded domain in $\mathbb{R}^2 $. Error estimates are obtained which show that these methods are second order correct in time. When $\Omega $ is a rectangle, alternating-direction Galerkin methods are discussed.

Asymptotic behavior of solutions of second order parabolic partial differential equations with unbounded coefficients

The implementation of a distributed digital logic simulation algorithm on a network of workstations is presented. The simulation of digital circuits is done using a demand driven approach. The simulation is performed on a network of workstations using the Parallel Virtual Machine (PVM) software. The use of the PVM as a platform for parallel and distributed applications is assessed in the light of the test results. The feasibility of running parallel logical simulation algorithms on such platform is considered.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

In this paper, the fourth order parabolic partial differential equation, that governs the behavior of a vibrating beam, is solved by using the Adomian Decomposition Method. The solution is derived in the form of a power series with easily computable components. The nonhomogeneous problem is quickly solved by observing the self-canceling noise terms whose sum vanishes in the limit. Comparing this methodology with some known techniques shows that the present approach is highly accurate.

Optimality of the boundary control for [formula omitted] parabolic lag system

Image coding is imperative for successful implementation of visual communications applications. With the emergence of enhanced multimedia technology, numerous video applications have emerged like multimedia conferencing, video on demand and DVDs. Coding is an essential component of all video applications and it becomes necessary to have improved coding techniques for faster applications. This paper discusses how parallel video coding on load balanced multi-processor systems can help in incorporating efficient coding techniques like vector quantization into practical applications. Two parallel processing platforms will be discussed namely the heterogeneous network of workstations and the TI C40 DSP chips. The software platforms used for these are the Parallel Virtual Machines (PVM) programming model and Parallel C respectively. An integration of the two programming models by using a PVM to Parallel C translation and the effect of load balancing for improved performance will also be discussed.

The static task allocation problem and the dynamic load balancing problem are relevant issues when using a multiprocessor computer system to execute parallelized tasks. The paper presents an artificial intelligence (AI) strategy to resolve the above problems under the PVM (Parallel Virtual Machine). The AI technique is employed to accurately predict better allocation for PVM tasks in their initial assignment as well as execution time. The performance characteristics of the proposed load balancing algorithm are also examined for a homogeneously distributed system. Three different problems are implemented for evaluating our algorithm's performance. Experimental results demonstrate that our algorithm performs more effectively than conventional approaches.

An arms race with deteriorating armaments

Unconditionally stable high accuracy compact difference schemes for multi-space dimensional vibration problems with simply supported boundary conditions

Chebyshev series solution of the two dimensional heat equation

We introduce a class of interesting stochastic processes based on Brownian-time processes. These are obtained by taking Markov processes and replacing the time parameter with the modulus of Brownian motion. They generalize the iterated Brownian motion (IBM) of Burdzy and the Markov snake of Le Gall, and they introduce new interesting examples. After defining Brownian-time processes, we relate them to fourth order parabolic partial differential equations (PDE’s). We then study their exit problem as they exit nice domains in $\mathbb{R}^d$ , and connect it to elliptic PDE’s. We show that these processes have the peculiar property that they solve fourth order parabolic PDE’s, but their exit distribution—at least in the standard Brownian time process case—solves the usual second order Dirichlet problem. We recover fourth order PDE’s in the elliptic setting by encoding the iterative nature of the Brownian-time process, through its exit time, in a standard Brownian motion. We also show that it is possible to assign a formal generator to these non-Markovian processes by giving such a generator in the half-derivative sense.

Stack filters are a class of nonlinear spatial operators used for noise suppression. Their design is formulated as an optimisation problem and genetic algorithms (GAs) are used to perform the configuration. Applying the mean absolute error (MAE) as the basis of an objective function, the stack filter is used to restore magnetic resonance (MR) images corrupted with uncorrelated additive noise from 10% and 50%. The filter is trained on corresponding patches of the original and noisy image and then applied to the whole image. The outcomes are compared with the median filter and return a smaller MAE for all noise levels. The dependency of MAE on training window size and GA early termination is examined, showing that a reduction of 75% in computational complexity can be achieved by a 10% relaxation in MAE. The design is then extended from 9-point to 13-point filters and by training on Poisson noise, the filter is applied to nuclear medicine bone scans where no absolute truth exists. Surface topology, image profiles and the measurement of relative contrast show its value in reducing noise whilst preserving contrast. Because of its computational complexity the process has been implemented as a distributed GA using the parallel virtual machine (PVM) software.

Muscle stress is a disease resulting from performing too much effort, sport fitness or high blood pressure problem. This paper focuses on the mathematical modeling of crack propagation that applies on the human muscle stress. The modeling describes the relationship between the temperature and crack propagation among targeted region, node and element. The contribution of this paper is to predict the stress level based on the mathematical modeling for nodal displacements of crack propagation with respect to time and space. The initial stage of crack propagation that leads to an initiation of crack propagation is being modeled satisfactorily the discretization of finite element method (FEM) of the specific region. The sequential algorithm of the crack propagation simulation for muscle stress will be solve using Gauss Seidel method (GS). The parallel algorithm will be solve using Red Black Gauss Seidel method (RBGS). Some comparisons based on numerical analysis and parallel performance evaluation will be analyzed in terms of convergent rate of FE solution results to the exact solution of clinical dataset, run time prediction, time execution, speedup, efficiency, effectiveness and temporal performance. C and Parallel Virtual Machine (PVM) software with open source operating system on distributed parallel computer system (DPCS) will support the huge computation cost of the parallel algorithms.