This paper proposed the several real life applications for big data analytic using parallel computing software. Some parallel computing software under consideration are Parallel Virtual Machine, MATLAB Distributed Computing Server and Compute Unified Device Architecture to simulate the big data problems. The parallel computing is able to overcome the poor performance at the runtime, speedup and efficiency of programming in sequential computing. The mathematical models for the big data analytic are based on partial differential equations and obtained the large sparse matrices from discretization and development of the linear equation system. Iterative numerical schemes are used to solve the problems. Thus, the process of computational problems are summarized in parallel algorithm. Therefore, the parallel algorithm development is based on domain decomposition of problems and the architecture of difference parallel computing software. The parallel performance evaluations for distributed and shared memory architecture are investigated in terms of speedup, efficiency, effectiveness and temporal performance.

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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.

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