Abstract: High computational complexity problem, high computational cost and deal with a big data are the motivation to study the physical and chemical properties of benzene. Based on the limitation of memory system, processor speed and huge time step computation, we propose the implementation of parallel Gaussian suites of program, particularly the program dealing with high order Møller–Plesset perturbation theory, on high performance homogeneous computing platform (HPC) for predicting the physical and chemical properties of small to medium size molecules, such as benzene, the subject of the present work. Besides high accuracy of the geometrical parameters that can be offered by MP4 simulation, orbital shapes, HOMO-LUMO energy gaps and spectral properties of the molecule are among the properties that can be obtained with accurate prediction. In order to achieve high performance indicators, we need to execute the program in multiple instruction and data stream (MIMD) paradigm using homogenous processors architecture. At the end of this paper, it is shown that Parallel algorithm of Gaussian program using the Linda software can be executed and is well suited in both homogenous and heterogeneous processors. The performance evaluation is essentially based on run time, temporal performance, effectiveness, efficiency, and speedup.

Norma Alias, Riadh Sahnoun and Nur Fatin Kamila Zanalabidin, “On the Prediction of Properties of Benzene using MP4 Method Executed on High Performance Computing with Heterogeneous Platform” International Journal of Advanced Computer Science and Applications(IJACSA), 10(11), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0101119

@article{Alias2019,
title = {On the Prediction of Properties of Benzene using MP4 Method Executed on High Performance Computing with Heterogeneous Platform},
journal = {International Journal of Advanced Computer Science and Applications}
doi = {10.14569/IJACSA.2019.0101119},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0101119},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {11},
author = {Norma Alias and Riadh Sahnoun and Nur Fatin Kamila Zanalabidin},
}