Fadi Fouz

Work place: Department of Computer Science, King abdulaziz University Jeddah, Saudi Arabia

E-mail: ffouz@hotmail.com


Research Interests: Software Development Process, Software Engineering, Autonomic Computing, Distributed Computing


Fadi Fouz received M.Sc degree in Electronic Engineering from Warsaw Technical University, Polnad in 1974 and Ph.D degree in computer science from University of Sheffield England in 1981. He is a full professor with computer Science dept, Faculty of Computing and Information technology, King Abdullaziz University, Saudi Arabia. His research interests include agent based software engineering, cloud computing, software engineering, big data, distributed systems, exascale system testing.

Author Articles
Empirical Analysis of HPC Using Different Programming Models

By Muhammad Usman Ashraf Fadi Fouz Fathy Alboraei Eassa

DOI: https://doi.org/10.5815/ijmecs.2016.06.04, Pub. Date: 8 Jun. 2016

During the last decade, Heterogeneous systems are emerging for high performance computing [1]. In order to achieve high performance computing (HPC), existing technologies and programming models aims to see rapid growth toward intra-node parallelism [2]. The current high computational system and applications demand for a massive level of computation power. In last few years, Graphical processing unit (GPU) has been introduced an alternative of conventional CPU for highly parallel computing applications both for general purpose and graphic processing. Rather than using the traditional way of coding algorithms in serial by single CPU, many multithreading programming models has been introduced such as CUDA, OpenMP, and MPI to make parallel processing by using multicores. These parallel programming models are supportive to data driven multithreading (DDM) principle [3]. In this paper, we have presented performance based preliminary evaluation of these programming models and compared with the conventional single CPU serial processing system. We have implemented a massive computational operation for performance evaluation such as complex matrix multiplication operation. We used data driven multithreaded HPC system for performance evaluation and presented the results with a comprehensive analysis of these parallel programming models for HPC parallelism.

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