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Methodology for predicting the energy consumption of SPMD application on virtualized environments
Keywords:
Performance, Energy, EDP, Prediction, VirtualizationAbstract
Over the last decade, the computing clusters have been updated in order to satisfy the increasing demand of greater computational power for running applications. However, this increasing is transformed in more system en- ergy consumption, which results in financial, environmental and in some cases with social consequences. Hence, the ideal is to achieve an scenario that allows the system admin- istrator to find a trade-off between time and energy-efficiency for parallel algorithms on virtualized environments. The main objective of this work is based on developing an analytical model to predict the energy consumption and energy delay product (EDP) for SPMD applications on virtual environments. The SPMD applications selected are designed through a message passing interface (MPI) library with high communication volumes, which can generate im- balance issues that affect seriously the execution time and also the energy-efficiency. Our method is composed by four phases (characterization, tile distribution model, mapping and scheduling). This method has been validated using scientific applications and we observe that the minimum Energy and EDP values are located close to the values calculated with our analytical model with an error rate between 4% and 9%.
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References
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[2] A. Beloglazov and R. Buyya, “Energy efficient resource management in virtualized cloud data centers,” in 10th IEEE/ACM Int Conf on Cluster, Cloud and Grid Computing (CCGrid), 2010, pp. 826–831.
[3] J. Ekanayake and G. Fox, “High performance parallel computing with clouds and cloud technologies,” Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, vol. 34, pp. 20–38, 2010.
[4] G. Mercier and J. Clet-Ortega, “Towards an efficient process placement policy for mpi applications in multicore environments,” vol. 5759, pp. 104–115, 2009.
[5] A. Iosup, S. Ostermann, N. Yigitbasi, R. Prodan, T. Fahringer, and D. Epema, “Performance analysis of cloud computing services for many-tasks scientific computing,” IEEE Trans. Parallel Distrib. Syst., vol. 22, no. 6, pp. 931–945, June 2011.
[6] R. Muresano, D. Rexachs, and E. Luque, “Methodology for efficient execution of spmd applications on multicore environments,” 10th IEEE/ACM Int Conf on Cluster, Cloud and Grid Comp, CCGrid 2010, Australia, pp. 185–195, 2010.
[7] J. Balladini, R. Suppi, D. Rexachs, and E. Luque, “Impact of parallel programming models and cpus clock frequency on energy consumption of hpc systems,” pp. 16–21, 2011.
[8] R. Gonzalez and M. Horowitz, “Energy dissipation in general purpose microprocessors,” Solid-State Circuits, IEEE Journal of, vol. 31, no. 9, pp. 1277–1284, 1996.
[9] R. F. V. der Wijngaart Ana Haoqiang Jin, “Nas parallel benchmarks, multi-zone versions,” NASA Advanced Supercomputing (NAS) Division, Tech. Rep., 2003.
[10] R. Muresano, D. Rexachs, and E. Luque, “Combining scalability and efficiency for spmd applications on multicore clusters,” The 2011 International Conference on Parallel and Distributed Processing Techniques and Applications, Las Vegas, USA, pp. 43–49, 2011.
[11] F. Schatz, S. Koschnicke, N. Paulsen, C. Starke, and M. Schimmler, “Mpi performance analysis of amazon ec2 cloud services for high performance computing,” Advances in Computing and Communications, vol. 190, pp. 371–381, 2011.
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Published
2018-04-13
How to Cite
Balladini, J., Muresano, R., Suppi, R., Rexachs del Rosario, D., & Luque Fadón, E. (2018). Methodology for predicting the energy consumption of SPMD application on virtualized environments. Journal of Computer Science and Technology, 13(03), p. 130–136. Retrieved from https://journal.info.unlp.edu.ar/JCST/article/view/591
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