Predicting the Performance of MPI Applications over Different Grid Architectures

Main Article Content

Ahmed Badri Muslim Fanfakh

Abstract

Nowadays, the high speed and accurate optimization algorithms are required. In most of the cases, researchers need a method to predict some criteria with acceptable accuracy to use it after in their algorithms. However, in the field of parallel computing the execution time can be considered the most important criteria. Consequently, this paper presents new execution time prediction model for message passing interface applications execute over numerous grid scenarios. The model has ability to predict the execution time of the message passing applications running over any grid configuration in term of different number of nodes and their computing powers. The experiments are evaluated over SimGrid simulator to simulate the grid configuration scenarios. The results of comparing the real and the predicted execution time show a good accuracy. The average error ratio between the real and the predicted execution time for three benchmarks are 4.36%, 5.79% and 6.81%.

Downloads

Download data is not yet available.

Article Details

How to Cite
[1]
A. B. M. Fanfakh, “Predicting the Performance of MPI Applications over Different Grid Architectures”, JUBPAS, vol. 27, no. 1, pp. 468-477, Apr. 2019.
Section
Articles

References

1. V. Rajaraman and R. A. M. M. C. SIVA, Parallel Computers Architecture and Programming. PHI Learning Pvt. Ltd., 2016.

2. C.-Y. Chou, H.-Y. Chang, S.-T. Wang, K.-C. Huang, and C.-Y. Shen, “An improved model for predicting HPL performance,” in International Conference on Grid and Pervasive Computing, 2007, pp. 158–168.‏

3. Xu, Z., & Hwang, K.: Modeling communication overhead: MPI and MPL performance on the IBM SP2. IEEE Parallel & Distributed Technology, Systems & Applications, Vol. 4, No. 1, pp. 9-24, 1996.‏

4. B. Subramaniam and W. Feng, “Statistical power and performance modeling for optimizing the energy efficiency of scientific computing,” in Proceedings of the 2010 IEEE/ACM Int’l Conference on Green Computing and Communications & Int’l Conference on Cyber, Physical and Social Computing, 2010, pp. 139–146. ‏

5. B. C. Lee, D. M. Brooks, B. R. de Supinski, M. Schulz, K. Singh, and S. A. McKee, “Methods of inference and learning for performance modeling of parallel applications,” in Proceedings of the 12th ACM SIGPLAN symposium on Principles and practice of parallel programming, 2007, pp. 249–258.

6. K. Singh, E. İpek, S. A. McKee, B. R. de Supinski, M. Schulz, and R. Caruana, “Predicting parallel application performance via machine learning approaches,” Concurr. Comput. Pract. Exp., vol. 19, no. 17, pp. 2219–2235, 2007.

7. B. J. Barnes, B. Rountree, D. K. Lowenthal, J. Reeves, B. De Supinski, and M. Schulz, “A regression-based approach to scalability prediction,” in Proceedings of the 22nd annual international conference on Supercomputing, 2008, pp. 368–377.

8. J. L. Hennessy and D. A. Patterson, Computer architecture: a quantitative approach. Elsevier, 2011.

9. B. Miegemolle and T. Monteil, “Hybrid Method to Predict Execution Time of Parallel Applications.,” in CSC, 2008, pp. 224–230.

10. A. Jayakumar, P. Murali, and S. Vadhiyar, “Matching application signatures for performance predictions using a single execution,” in 2015 IEEE International Parallel and Distributed Processing Symposium, 2015, pp. 1161–1170.‏

11. J. C. Charr, R. Couturier, A. Fanfakh, and A. Giersch, “Dynamic frequency scaling for energy consumption reduction in synchronous distributed applications,” in 2014 IEEE International Symposium on Parallel and Distributed Processing with Applications, 2014, pp. 225–230.

12. J.-C. Charr, R. Couturier, A. Fanfakh, and A. Giersch, “Energy consumption reduction with DVFS for message passing iterative applications on heterogeneous architectures,” in 2015 IEEE International Parallel and Distributed Processing Symposium Workshop, 2015, pp. 922–931.

13. A. Fanfakh, J.-C. Charr, R. Couturier, and A. Giersch, “Optimizing the energy consumption of message passing applications with iterations executed over grids,” J. Comput. Sci., vol. 17, pp. 562–575, 2016.

14. A. B. M. Fanfakhri, A. Y. Yousif, and E. Alwan, “Multi-objective Optimization of Grid Computing for Performance, Energy and Cost,” Kurdistan J. Appl. Res., vol. 2, no. 3, pp. 74–79, 2017.

15. A. Fanfakh, J.-C. Charr, R. Couturier, and A. Giersch, “CPUs Energy Consumption Reduction for Asynchronous Parallel Methods Running over Grids,” in 2016 IEEE Intl Conference on Computational Science and Engineering (CSE) and IEEE Intl Conference on Embedded and Ubiquitous Computing (EUC) and 15th Intl Symposium on Distributed Computing and Applications for Business Engineering (DCABES), 2016, pp. 205–212.

16. S. K. Idrees and A. B. M. Fanfakh, “Performance and Energy Consumption Prediction of Randomly Selected Nodes in Heterogeneous Cluster,” in International Conference on New Trends in Information and Communications Technology Applications, 2018, pp. 21–34.

17. H. Casanova, A. Legrand, and M. Quinson, “Simgrid: A generic framework for large-scale distributed experiments,” in Tenth International Conference on Computer Modeling and Simulation (uksim 2008), 2008, pp. 126–131.

18. N. A. S. P. Benchmarks and M. Versions, “NASA Advanced Supercomputing Division,” NASA Ames Res. Center, CA, USA, 2003.