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Hybrid Model Based Testing Tool Architecture for Exascale Computing System
Muhammad Usman Ashraf, Fathy Elbouraey Eassa
Pages - 245 - 252     |    Revised - 31-08-2015     |    Published - 30-09-2015
Volume - 9   Issue - 5    |    Publication Date - September / October 2015  Table of Contents
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KEYWORDS
Exascale Computing, GPU, CUDA, OpenMP, Parallelism, High Performance Computing (HPC).
ABSTRACT
Exascale computing refers to a computing system which is capable to at least one exaflop in next couple of years. Many new programming models, architectures and algorithms have been introduced to attain the objective for exascale computing system. The primary objective is to enhance the system performance. In modern/super computers, GPU is being used to attain the high computing performance. However, it’s the objective of proposed technologies and programming models is almost same to make the GPU more powerful. But these technologies are still facing the number of challenges including parallelism, scale and complexity and also many more that must be fixed to achieve make computing system more powerful and efficient. In this paper, we have present a testing tool architecture for a parallel programming approach using two programming models as CUDA and OpenMP. Both CUDA and OpenMP could be used to program shared memory and GPU cores. The object of this architecture is to identify the static errors in the program that occurred during writing the code and cause absence of parallelism. Our architecture enforces the developers to write the feasible code through we can avoid from the essential errors in the program and run successfully.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 Scribd 
5 SlideShare 
6 PdfSR 
1 J. J. Shalf , S. Dosanjh, and J. Morrison, “Exascale Computing Technology Challenges”. Springer-Verlag Berlin Heidelberg. Pp. 1-25. 2011.
2 J. M. Yusof et al, “Exploring weak scalability for FEM calculations on a GPU-Enhanced cluster”, 33.685–699. Nov, 2007.
3 C.T. Yang, C.L. Huang and C.F. Lin, “Hybrid CUDA, OpenMP, and MPI parallel programming on multicore GPU”. Computer Physics Communications. Pp. 266-269. 2011.
4 M. Suß and C. Leopold, “Common Mistakes in OpenMP and How To Avoid Them”. 2007.
5 J.P. Hoeflinger and B.R. Supinski, “ The OpenMP memory model”. In: Proceedings of the First International Workshop on OpenMP - IWOMP .2005.
6 D. A. Mey and T. Reichstein, “Parallelization with OpenMP and MPI A Simple Example (Fortran)”. Oct, 2007
7 M. Zheng, V.T. Ravi, F. Qin, and G. Agrawal , “GRace: A Low-Overhead Mechanism for Detecting Data Races in GPU Programs”. ACM. Dec, 2011.
8 G. Hager, G. Jost and R. Rabenseifner “Communication Characteristics and Hybrid MPI/OpenMP Parallel Programming on Clusters of Multi-core SMP Nodes”. Cray User Group Proceedings. 2009.
9 D. Shreiner, M. Woo, J. Neider and T. Davis, “OpenGL(R) Programming Guide: The Official Guide to Learning OpenGL(R)”, Version 2.1, 6th edition, Addison–Wesley Professional, Reading, MA, ISBN 0321481003, 2007.
10 J. Gustedt, “Parallelizing nested loop in OpenMP,”,http://stackoverflow.com/questions/19193725/ parallelizing-nested-loop-inopenmp-using-pragma-parallel-for-shared, Oct. 5, 2013 [May 10, 2015]
11 Alrikai, “CUDA racecheck,”http://stackoverflow.com/questions/13861017/cuda-racecheckshared-memory-array-and-cudadevicesynchronize, Jan. 11,2013 [April 22, 2015]
12 Daedalus, “How do CUDA blocks/threads map onto CUDA cores,”http://stackoverflow.com/questions/ 10460742/how-do-cuda-blocks-warps-threadsmap-onto-cuda-cores,. May 5, 2012 [May 14, 2015].
Mr. Muhammad Usman Ashraf
Faculty of Information and Computer Technology Department of Computer Science King Abdulaziz University Jeddah, 21577 , Saudi Arabia - Saudi Arabia
m.usmanashraf@yahoo.com
Mr. Fathy Elbouraey Eassa
Faculty of Information and Computer Technology Department of Computer Science King Abdulaziz University Jeddah, 21577 , Saudi Arabia - Saudi Arabia