High speed computing of ice thickness equation for ice sheet model

Two-dimensional (2-D) ice flow thermodynamics coupled model acts as a vital role for visualizing the ice sheet behaviours of the Antarctica region and the climate system. One of the parameters used in this model is ice thickness. Explicit method of finite difference method (FDM) is used to discretize the ice thickness equation. After that, the equation will be performed on Compute Unified Device Architecture (CUDA) programming by using Graphics Processing Unit (GPU) platform. Nowadays, the demand of GPU for solving the computational problem has been increasing due to the low price and high performance computation properties. This paper investigates the performance of GPU hardware supported by the CUDA parallel programming and capable to compute a large sparse complex system of the ice thickness equation of 2D ice flow thermodynamics model using multiple cores simultaneously and efficiently. The parallel performance evaluation (PPE) is evaluated in terms of execution time, speedup, efficiency, effectiveness and temporal performance

Tagging narrator’s names in Hadith text

No AbstractKeywords: tagging; hadith text; nam

Integration of a big data emerging on large sparse simulation and its application on green computing platform

The process of analyzing large data and verifying a big data set are a challenge for understanding the fundamental concept behind it. Many big data analysis techniques suffer from the poor scalability, variation inequality, instability, lower convergence, and weak accuracy of the large-scale numerical algorithms. Due to these limitations, a wider opportunity for numerical analysts to develop the efficiency and novel parallel algorithms has emerged. Big data analytics plays an important role in the field of sciences and engineering for extracting patterns, trends, actionable information from large sets of data and improving strategies for making a decision. A large data set consists of a large-scale data collection via sensor network, transformation from signal to digital images, high resolution of a sensing system, industry forecasts, existing customer records to predict trends and prepare for new demand. This paper proposes three types of big data analytics in accordance to the analytics requirement involving a large-scale numerical simulation and mathematical modeling for solving a complex problem. First is a big data analytics for theory and fundamental of nanotechnology numerical simulation. Second, big data analytics for enhancing the digital images in 3D visualization, performance analysis of embedded system based on the large sparse data sets generated by the device. Lastly, extraction of patterns from the electroencephalogram (EEG) data set for detecting the horizontal-vertical eye movements. Thus, the process of examining a big data analytics is to investigate the behavior of hidden patterns, unknown correlations, identify anomalies, and discover structure inside unstructured data and extracting the essence, trend prediction, multi-dimensional visualization and real-time observation using the mathematical model. Parallel algorithms, mesh generation, domain-function decomposition approaches, inter-node communication design, mapping the subdomain, numerical analysis and parallel performance evaluations (PPE) are the processes of the big data analytics implementation. The superior of parallel numerical methods such as AGE, Brian and IADE were proven for solving a large sparse model on green computing by utilizing the obsolete computers, the old generation servers and outdated hardware, a distributed virtual memory and multi-processors. The integration of low-cost communication of message passing software and green computing platform is capable of increasing the PPE up to 60% when compared to the limited memory of a single processor. As a conclusion, large-scale numerical algorithms with great performance in scalability, equality, stability, convergence, and accuracy are important features in analyzing big data simulation

Performance of modified non-linear shooting method for simulation of 2nd order two-point BVPS

In this research article, numerical solution of nonlinear 2nd order two-point boundary value problems (TPBVPs) is discussed by the help of nonlinear shooting method (NLSM), and through the modified nonlinear shooting method (MNLSM). In MNLSM, fourth order Runge-Kutta method for systems is replaced by Adams Bashforth Moulton method which is a predictor-corrector scheme. Results acquired numerically through NLSM and MNLSM of TPBVPs are discussed and analyzed. Results of the tested problems obtained numerically indicate that the performance of MNLSM is rapid and provided desirable results of TPBVPs, meanwhile MNLSM required less time to implement as comparable to the NLSM for the solution of TPBVPs

Integration of a big data emerging on large sparse simulation and its application on green computing platform

The process of analyzing large data and verifying a big data set are a challenge for understanding the fundamental concept behind it. Many big data analysis techniques suffer from the poor scalability, variation inequality, instability, lower convergence, and weak accuracy of the large-scale numerical algorithms. Due to these limitations, a wider opportunity for numerical analysts to develop the efficiency and novel parallel algorithms has emerged. Big data analytics plays an important role in the field of sciences and engineering for extracting patterns, trends, actionable information from large sets of data and improving strategies for making a decision. A large data set consists of a large-scale data collection via sensor network, transformation from signal to digital images, high resolution of a sensing system, industry forecasts, existing customer records to predict trends and prepare for new demand. This paper proposes three types of big data analytics in accordance to the analytics requirement involving a large-scale numerical simulation and mathematical modeling for solving a complex problem. First is a big data analytics for theory and fundamental of nanotechnology numerical simulation. Second, big data analytics for enhancing the digital images in 3D visualization, performance analysis of embedded system based on the large sparse data sets generated by the device. Lastly, extraction of patterns from the electroencephalogram (EEG) data set for detecting the horizontal-vertical eye movements. Thus, the process of examining a big data analytics is to investigate the behavior of hidden patterns, unknown correlations, identify anomalies, and discover structure inside unstructured data and extracting the essence, trend prediction, multi-dimensional visualization and real-time observation using the mathematical model. Parallel algorithms, mesh generation, domain-function decomposition approaches, inter-node communication design, mapping the subdomain, numerical analysis and parallel performance evaluations (PPE) are the processes of the big data analytics implementation. The superior of parallel numerical methods such as AGE, Brian and IADE were proven for solving a large sparse model on green computing by utilizing the obsolete computers, the old generation servers and outdated hardware, a distributed virtual memory and multi-processors. The integration of low-cost communication of message passing software and green computing platform is capable of increasing the PPE up to 60% when compared to the limited memory of a single processor. As a conclusion, large-scale numerical algorithms with great performance in scalability, equality, stability, convergence, and accuracy are important features in analyzing big data simulation

Antimicrobial activities of marine fungi from Malaysia

Copyright 2011 Elsevier B.V., All rights reserved.Peer reviewedPublisher PD

High-performance computing and communication models for solving the complex interdisciplinary problems on DPCS

The paper presents some advanced high performance (HPC) and parallel computing (PC) methodologies for solving a large space complex problem involving the integrated difference research areas. About eight interdisciplinary problems will be accurately solved on multiple computers communicating over the local area network. The mathematical modeling and a large sparse simulation of the interdisciplinary effort involve the area of science, engineering, biomedical, nanotechnology, software engineering, agriculture, image processing and urban planning. The specific methodologies of PC software under consideration include PVM, MPI, LUNA, MDC, OpenMP, CUDA and LINDA integrated with COMSOL and C++/C. There are different communication models of parallel programming, thus some definitions of parallel processing, distributed processing and memory types are explained for understanding the main contribution of this paper. The matching between the methodology of PC and the large sparse application depends on the domain of solution, the dimension of the targeted area, computational and communication pattern, the architecture of distributed parallel computing systems (DPCS), the structure of computational complexity and communication cost. The originality of this paper lies in obtaining the complex numerical model dealing with a large scale partial differential equation (PDE), discretization of finite difference (FDM) or finite element (FEM) methods, numerical simulation, high-performance simulation and performance measurement. The simulation of PDE will perform by sequential and parallel algorithms to visualize the complex model in high-resolution quality. In the context of a mathematical model, various independent and dependent parameters present the complex and real phenomena of the interdisciplinary application. As a model executes, these parameters can be manipulated and changed. As an impact, some chemical or mechanical properties can be predicted based on the observation of parameter changes. The methodologies of parallel programs build on the client-server model, slave-master model and fragmented model. HPC of the communication model for solving the interdisciplinary problems above will be analyzed using a flow of the algorithm, numerical analysis and the comparison of parallel performance evaluations. In conclusion, the integration of HPC, communication model, PC software, performance and numerical analysis happens to be an important approach to fulfill the matching requirement and optimize the solution of complex interdisciplinary problems

Synthesis, Spectral Studies, and Theoretical Treatment of New Ni(II),and Co(II) Complexes of Bidenetate Ligands 2-Benzamido Benzothiazole ,and 2-Actamido Benzothiazole

New metal complexes of the ligands 2-benzamido benzothiazole(B1), and 2-actamido benzothiazole(B2) with metal ions Ni(II),and Co(II) were prepared in alcoholic medium. The prepared complexes were characterized by FT-IR and electronic spectroscopy, Magnetic susceptibility, Flame Atomic Absorption technique as well as elemental analysis and conductivity measurement. From the spectral studies, an octahedral monomer structure proposed for Ni(II) complexes, and a tetrahedral monomer structure for Co(II)complexes.Semi-empirical methods (PM3,and ZINDO/1)were carried out to evaluate the heat formation( ?H?f)binding energy(?Eb) and dipole moment(µ)for all metal complexes. Also vibration frequencies, Electrostatic potential, HOMO and LUMO energies for ligands were calculated

Numerical analysis of spray-dic modeling for fruit concentration drying process into powder based on computational fluid dynamic

The drying process is most popular preservation methods. It is important in producing the powder and natural dye by concentration fruit drying. Spray-DIC is one of the concentration techniques for drying process using the nozzle flow application in computational fluid dynamic. The mathematical modeling for drying process in this paper includes mass conservation and energy conservation of fruit concentration based on partial differential equation. The discretization of mathematical model will use the finite difference method with the initial and boundary conditions of nozzle flow application. The mathematical modeling computes numerical in sequential algorithm. Jacobi and Gauss-Seidel scheme will use to solve the linear system of mathematical modeling. The execution time, no of iteration, accuracy, root mean square error and maximum error are measured for investigating the numerical analysis. The results show the Gauss Seidel method is the alternative method compared to Jacobi method for solving the Spray-DIC modeling

Sequential algorithm and numerical analysis on mathematical model for thermal control curing process of thermoset composite materials

To reproduce and improve the efficiency of waste composite materials with consistence and high quality, it is important to tailor and control their temperature profile during curing process. Due to this phenomenon, temperature profile during curing process between two layers of composite materials, which are, resin and carbon fibre are visualized in this paper. Thus, mathematical model of 2D convection-diffusion of the heat equation of thick thermoset composite during its curing process is employed for this study. Sequential algorithms for some numerical approximation such as Jacobi and Gauss Seidel are investigated. Finite difference method schemes such as forward, backward and central methods are used to discretize the mathematical modelling in visualizing the temperature behavior of composite materials. While, the physical and thermal properties of materials used from previous studies are fully employed. The comparisons of numerical analysis between Jacobi and Gauss Seidel methods are investigated in terms of time execution, iteration numbers, maximum error, computational and complexity, as well as root means square error (RMSE). The Fourth-order Runge-Kutta scheme is applied to obtain the degree of cure for curing process of composite materials. From the numerical analysis, Gauss Seidel method gives much better output compared to Jacobi method