Empirical Investigation Of Cloud, Grid and Virtualization Using Compilar Optimization Level For Threads Processe
Keywords:
Cloud Computing, Compiler Optimization, Grid, Thread ProcessesAbstract
This research focused on the implementation of Open MP. It considers the parallelization of an application code that simulates the thermal gradient of material in two dimensions. A ‘C language’ program code called jacobi2d.c that solves a rectangular 2-dimensional heat conductivity problem using Jacobi iterative method was used. The boundary conditions required to compute a temperature distribution for a rectangular 2D problem are: Top 300C, Bottom 500C, Left 400C and Right 900C with a range of problem sizes enter as a run-time parameter to alter the problem sizes and convergence criteria. Also, there were computations and readings for iterations and runtime for four values of M and N which were selected for 01, 02, and 03 optimizations. In Table 1 Readings, four values were selected for each of the iterations. The results show the performance of the runtime as the processor increases from 01-optimization, to 02-optimization and finally to 03- optimization. It can be deduced from the representation that the run time of the values reduces as more resources are allocated to execution through the increase in optimization level. Also, in Table 2 Readings, the runtime decreases as it moves from thread1, thread2, thread3, and thread4, comparing the last values for thread1 which are M is 180, N is 200, and their runtime which is 42.797187001. Also the last values for thread2 which are M is 180, N is 200, their runtime which is 21.772106003. When the two runtimes were compared, it was discovered that there was a decrease in the runtime because the more the thread increases, the more system resources they share such as a processor which may affect their runtime by increasing it.
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References
parallel implementation of the k nearest neighbours classifier in three levels: Threads, mpi processes and the grid. In International Conference on High Performance Computing for Computational Science (pp. 225-235). Springer, Berlin, Heidelberg.
Chiueh, S. N. T. C., & Brook, S. (2005). A survey on virtualization technologies. Rpe Report, 142.
Cafaro, M., & Aloisio, G. (2011). Grids, clouds, and virtualization. In Grids, Clouds and Virtualization (pp. 1-21). Springer, London.
Dagum, L., & Menon, R. (1998). OpenMP: an industry standard API for shared-memory programming. Computational Science & Engineering, IEEE, 5(1), 46-55.
Foster, I., Zhao, Y., Raicu, I., & Lu, S. (2008, November). Cloud computing and grid computing 360-degree compared. In Grid Computing Environments Workshop, 2008. GCE'08 (pp. 1-10). Ieee
Foster, I., & Kesselman, C. (Eds.). (2003). The Grid 2: Blueprint for a new computing infrastructure. Elsevier. [7] Lombardi, F., & Di Pietro, R. (2011). Secure virtualization for cloud computing. Journal of Network and Computer Applications, 34(4), 1113-1122.
Lamport, L. (1979). How to make a multiprocessor computer that correctly executes multiprocess progranm. IEEE transactions on computers, (9), 690-691.
Mc Evoy, G. V., & Schulze, B. (2008, December). Using clouds to address grid limitations. In Proceedings of the 6th international workshop on Middleware for grid computing (p. 11). ACM.
Smith, R. (2009). Computing in the cloud. Research Technology Management, 52(5), 65-68.
Wang, L., Tao, J., Kunze, M., Castellanos, A. C., Kramer, D., & Karl, W. (2008, September). Scientific cloud computing: Early definition and experience. In High Performance Computing and Communications, 2008. HPCC'08. 10th IEEE International Conference on (pp. 825-830). IEEE.
Youseff, L., Butrico, M., & Da Silva, D. (2008, November). Toward a unified ontology of cloud computing. In Grid Computing Environments Workshop, 2008. GCE'08 (pp. 1-10). IEEE
