Noriza Satam

High Performance Nanotechnology Software (HPNS) for Parameter Characterization of Nanowire Fabrication and Nanochip System

This paper presents the high performance nanotechnology software (HPNS) to enable scientific researchers for predicting, visualizing and observing the temperature behavior and some parameters characteristics of nanochip system and nanowire fabrication. The analysis of scientific algorithms and high performance computing are searchable through a user friendly web-based system. This software will involve some mathematical modeling, numerical simulations and high performance computing technology to improve the accuracy of prediction, visualization quality and parallel performance indicators. The parameters involve on the thermal control process of nanowire fabrication are focuses on pressure, density, space, time control. The identification of the parameters influences the process of nanowire fabrication. The next focused are parameters characteristics of nanochip system. The parameters that involve are temperature, electromagnetic wave, space, time and other properties impact of the development of multilayer nanochip system. The implementations of parallel algorithms for solving the Partial Differential Equation of heat transfer and wave motion problems are based on large sparse parabolic and elliptic types. The discretization technique to obtain a large sparse linear system of equations is based on Finite Difference Method. The HPNS will support the supercomputing of numerical simulations and its repository using distributed memory architecture. The performance indicators will investigate the parallel programs using Parallel Virtual Machine (PVM) and C language on Linux operating system in terms of run time, accuracy, convergence, errors, speedup, efficiency and effectiveness. As a conclusion, HPNS will be an alternative software system to support huge computational complexity of the multilayer nanochip system and nanowire fabrication model.

Performance evaluation of multidimensional parabolic type problems on distributed computing systems

Parabolic Partial Differential Equations (PDE) are well suited to multiprocessor implementation. However, the performance of a parallel program can be damaged by the mismatches between the parallelism available in the application and that available in the architecture. Communication cost, memory requirements, execution time, implementation cost, and others from a problem specific function should be considered to estimate a parallel program. In this paper, we present an optimizing technique called granularity analysis to evaluate the parallel algorithms particularly AGE families without degrading the performances. The resultant granularity analysis scheme is appropriate for developing adaptive parallelism of declarative programming languages on multiprocessors. The results recommend that the proposed method can be used for performance estimation of parallel programs. Red Black Gauss Seidel (GSRB) is selected as the benchmark for the differences numerical methods.

Parallelization of Temperature Distribution Simulations for Semiconductor and Polymer Composite Material on Distributed Memory Architecture

The implementations of parallel algorithms in solving partial differential equations (PDEs) for heat transfer problems are based on the high performance computing using distributed memory architecture. In this paper, the parallel algorithms are exploited finite difference method in solving multidimensional heat transfer problem for semiconductor components and polymer composite materials. Parallel Virtual Machine (PVM) and C language based on Linux operating system are the platform to run the parallel algorithms. This research focused on Red-Black Gauss Seidel (RBGS) iterative method. Parallel performance evaluations in terms of speedup, efficiency, effectiveness, temporal performance and communication cost are analyzed.

Some parallel numerical methods in solving partial differential equations

This paper will discuss the solution of twodimensional partial differential equations (PDEs) using some parallel numerical methods namely Gauss Seidel and Red Black Gauss Seidel. The selected two-dimensional PDE to solve in this paper are of parabolic and elliptic type. Parallel Virtual Machine (PVM) is used in support of the communication among all microprocessors of Parallel Computing System. PVM is well known as a software system that enables a collection of heterogeneous computers to be used as coherent and flexible concurrent computational resource. The numerical results will be presented graphically and parallel performance measurement by Gauss Seidel and Red Gauss Seidel methods will be evaluated in terms of execution time, speedup, efficiency, effectiveness and temporal performance. Performance evaluations are critical as this paper aimed to fabricate an efficient Two-Dimensional PDE Solver (TDPDES). This new well-organized TDPDES technique will enhance the research and analysis procedure of many engineering and mathematic fields.

Parallel system for abnormal cell growth prediction based on fast numerical simulation

The paper focuses on a numerical method for detecting, visualizing and monitoring abnormal cell growth using large-scale mathematical simulations. The discretization of multi-dimensional partial differential equation (PDE) is based on finite difference method. The predictor system depending on users input data via a user interface, generating the initial and boundary condition generated from parabolic or elliptic type of PDE. The processing large sparse matrixes are based on multiprocessor computer systems for abnormal growth visualization. The multi-dimensional abnormal cell has produced the numerical analysis and understanding results at the target area for the potential improvement of detection and monitoring the growth. The development of the prediction system is the combinations of the parallel algorithms, open source software on Linux environment and distributed multiprocessor system. The paper ends with a concluding remark on the parallel performance evaluations and numerical analysis in reducing the execution time, communication cost and computational complexity.

RETRACTED ARTICLE: Some parallel numerical methods in solving partial differential equations

This paper will discuss the solution of twodimensional partial differential equations (PDEs) using some parallel numerical methods namely Gauss Seidel and Red Black Gauss Seidel. The selected two-dimensional PDE to solve in this paper are of parabolic and elliptic type. Parallel Virtual Machine (PVM) is used in support of the communication among all microprocessors of Parallel Computing System. PVM is well known as a software system that enables a collection of heterogeneous computers to be used as coherent and flexible concurrent computational resource. The numerical results will be presented graphically and parallel performance measurement by Gauss Seidel and Red Gauss Seidel methods will be evaluated in terms of execution time, speedup, efficiency, effectiveness and temporal performance. Performance evaluations are critical as this paper aimed to fabricate an efficient Two-Dimensional PDE Solver (TDPDES). This new well-organized TDPDES technique will enhance the research and analysis procedure of many engineering and mathematic fields.

Notice of Retraction Some parallel numerical methods in solving partial differential equations

This paper will discuss the solution of twodimensional partial differential equations (PDEs) using some parallel numerical methods namely Gauss Seidel and Red Black Gauss Seidel. The selected two-dimensional PDE to solve in this paper are of parabolic and elliptic type. Parallel Virtual Machine (PVM) is used in support of the communication among all microprocessors of Parallel Computing System. PVM is well known as a software system that enables a collection of heterogeneous computers to be used as coherent and flexible concurrent computational resource. The numerical results will be presented graphically and parallel performance measurement by Gauss Seidel and Red Gauss Seidel methods will be evaluated in terms of execution time, speedup, efficiency, effectiveness and temporal performance. Performance evaluations are critical as this paper aimed to fabricate an efficient Two-Dimensional PDE Solver (TDPDES). This new well-organized TDPDES technique will enhance the research and analysis procedure of many engineering and mathematic fields.

Parallelization of iterative and direct schemes for Keller-box method on distributed memory platform

In this paper, we present iterative schemes, specifically the conjugate gradient, and Gauss seidel red-black (GSRB) and direct schemes namely LU factorization and Gauss elimination for Keller-box scheme. The aim of this paper is to offer reasonable assessments and contrasts on behalf of the numerical experiments of these two schemes ported to run through Parallel Virtual Machine (PVM) on distributed memory platform. The computational complexity also presented for the comparison purpose, and the graphs of parallel evaluation in terms of speedup, efficiency, effectiveness and temporal performance are presented as well.