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Polish Information Processing Society
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Annals of Computer Science and Information Systems, Volume 8

Proceedings of the 2016 Federated Conference on Computer Science and Information Systems

Modeling energy consumption of parallel applications

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DOI: http://dx.doi.org/10.15439/2016F308

Citation: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 8, pages 855–864 ()

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Abstract. The paper presents modeling and simulation of energy consumption of two types of parallel applications: ge- ometric Single Program Multiple Data (SPMD) and divide-and- conquer (DAC). Simulation is performed in a new MERPSYS environment. Model of an application uses the Java language with extension representing message exchange between pro- cesses working in parallel. Simulation is performed by running threads representing distinct process codes of an application, with consideration of process counts. Instead of running time consuming calculations, their times are simulated using functions representing computational time dependent on input data sizes. The simulator considers performance and power consumption values for compute devices stored in its database. We performed verification of running the two applications on up to 1000 and 1024 processes respectively on a large cluster from Academic Computer Center in Gdansk demonstrating a high degree of accuracy between simulated and measured results.

References

  1. P. Czarnul, P. Rosciszewski, M. R. Matuszek, and J. Szymanski, “Simulation of parallel similarity measure computations for large data sets,” in 2nd IEEE International Conference on Cybernetics, CYBCONF 2015, Gdynia, Poland, June 24-26, 2015. IEEE, 2015. http://dx.doi.org/10.1109/CYBConf.2015.7175980. ISBN 978-1-4799-8322-3 pp. 472–477. 7175980
  2. W. McFadden, A. Nikolich, R. Parpart, and B. Runesha, “Saving on data center energy bills with edeals: Electricity demand-response easy adjusted load shifting,” in USENIX Workshop on Cool Topics on Sustainable Data Centers (CoolDC 16). Santa Clara, CA: USENIX Association, 2016. [Online]. Available: https://www.usenix.org/conference/cooldc16/workshop-program/presentation/mcfadden
  3. T. Cioara, I. Anghel, I. Salomie, D. Moldovan, G. Copil, and P. Plebani, “Dynamic consolidation methodology for optimizing the energy consumption in large virtualized service centers,” in Federated Conference on Computer Science and Information Systems - FedCSIS 2011, Szczecin, Poland, 18-21 September 2011, Proceedings, M. Ganzha, L. A. Maciaszek, and M. Paprzycki, Eds., 2011. ISBN 978-83-60810-22-4 pp. 1005–1011. [Online]. Available: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6078295
  4. H. Shoukourian, T. Wilde, A. Auweter, and A. Bode, “Predicting the energy and power consumption of strong and weak scaling hpc applications,” Supercomputing frontiers and innovations, vol. 1, no. 2, 2014. http://dx.doi.org/10.14529/jsfi140202.
  5. G. Lawson, M. Sosonkina, and Y. Shen, “Towards modeling energy consumption of xeon phi.” CoRR, vol. abs/1505.06539, 2015. [Online]. Available: http://dblp.uni-trier.de/db/journals/corr/corr1505.html#LawsonSS15
  6. A. Tiwari, M. A. Laurenzano, L. Carrington, and A. Snavely, “Modeling power and energy usage of hpc kernels,” in Parallel and Distributed Processing Symposium Workshops PhD Forum (IPDPSW), 2012 IEEE 26th International, May 2012. http://dx.doi.org/10.1109/IPDPSW.2012.121 pp. 990–998.
  7. F. Almeida, V. B. Pérez, A. C. Pérez, and J. Ruiz, “Modeling energy consumption for master-slave applications,” The Journal of Supercomputing, vol. 65, no. 3, pp. 1137–1149, 2013. http://dx.doi.org/10.1007/s11227-013-0914-y.
  8. C. Lively, X. Wu, V. Taylor, S. Moore, H.-C. Chang, and K. Cameron, “Energy and performance characteristics of different parallel implementations of scientific applications on multicore systems,” International Journal of High Performance Computing Applications, vol. 25, no. 3, pp. 342–350, 2011. http://dx.doi.org/10.1177/1094342011414749 Energy.
  9. R. Isidro-Ramirez, A. M. Viveros, and E. H. Rubio, “Energy consumption model over parallel programs implemented on multicore architectures,” International Journal of Advanced Computer Science and Applications(IJACSA), vol. 6, no. 6, 2015. http://dx.doi.org/10.14569/IJACSA.2015.060635.
  10. J. Proficz and P. Czarnul, Parallel Processing and Applied Mathematics: 11th International Conference, PPAM 2015, Krakow, Poland, September 6-9, 2015. Revised Selected Papers, Part II. Cham: Springer International Publishing, 2016, ch. Performance and Power-Aware Modeling of MPI Applications for Cluster Computing, pp. 199–209. ISBN 978-3-319-32152-3. [Online]. Available: http://dx.doi.org/10.1007/978-3-319-32152-3_19
  11. P. Czarnul and M. Matuszek, Parallel Processing and Applied Mathematics: 11th International Conference, PPAM 2015, Krakow, Poland, September 6-9, 2015. Revised Selected Papers, Part II. Cham: Springer International Publishing, 2016, ch. Considerations of Computational Efficiency in Volunteer and Cluster Computing, pp. 66–74. ISBN 978-3-319-32152-3. [Online]. Available: http://dx.doi.org/10.1007/978-3-319-32152-3_7
  12. L. A. Barroso and U. Hölzle, “The case for energy-proportional computing,” Computer, vol. 40, no. 12, pp. 33–37, Dec. 2007. http://dx.doi.org/10.1109/MC.2007.443.
  13. P. Balaprakash, A. Tiwari, and S. M. Wild, High Performance Computing Systems. Performance Modeling, Benchmarking and Simulation: 4th International Workshop, PMBS 2013, Denver, CO, USA, November 18, 2013. Revised Selected Papers. Cham: Springer International Publishing, 2014, ch. Multi Objective Optimization of HPC Kernels for Performance, Power, and Energy, pp. 239–260. ISBN 978-3-319-10214-6. [Online]. Available: http://dx.doi.org/10.1007/978-3-319-10214-6_12
  14. K. M. Tarplee, R. Friese, A. A. Maciejewski, and H. J. Siegel, “Efficient and scalable computation of the energy and makespan pareto front for heterogeneous computing systems,” in Computer Science and Information Systems (FedCSIS), 2013 Federated Conference on, Sept 2013, pp. 401–408.
  15. K. M. Tarplee, R. Friese, A. A. Maciejewski, and H. J. Siegel, “Efficient and Scalable Pareto Front Generation for Energy and Makespan in Heterogeneous Computing Systems,” in Recent Advances in Computational Optimization, S. Fidanova, Ed. Cham: Springer International Publishing, 2015, vol. 580, pp. 161–180. ISBN 978-3-319-12630-2 978-3-319-12631-9. [Online]. Available: http://link.springer.com/10.1007/978-3-319-12631-9_10
  16. A. Tiwari, M. A. Laurenzano, L. Carrington, and A. Snavely, “Auto-tuning for energy usage in scientific applications,” in Proceedings of the 2011 International Conference on Parallel Processing - Volume 2, ser. Euro-Par’11. Berlin, Heidelberg: Springer-Verlag, 2012. doi: 10.1007/978-3-642-29740-3_21. ISBN 978-3-642-29739-7 pp. 178–187. [Online]. Available: http://dx.doi.org/10.1007/978-3-642-29740-3_21
  17. P. Czarnul and P. Rosciszewski, “Optimization of execution time under power consumption constraints in a heterogeneous parallel system with gpus and cpus,” in Distributed Computing and Networking - 15th International Conference, ICDCN 2014, Coimbatore, India, January 4-7, 2014. Proceedings, ser. Lecture Notes in Computer Science, M. Chatterjee, J. Cao, K. Kothapalli, and S. Rajsbaum, Eds., vol. 8314. Springer, 2014. http://dx.doi.org/10.1007/978-3-642-45249-9_5. ISBN 978-3-642-45248-2 pp. 66–80.
  18. P. Rościszewski, P. Czarnul, R. Lewandowski, and M. Schally-Kacprzak, “KernelHive: a new workflow-based framework for multilevel high performance computing using clusters and workstations with CPUs and GPUs,” Concurrency and Computation: Practice and Experience, vol. 28, no. 9, pp. 2586–2607, Jun. 2016. http://dx.doi.org/10.1002/cpe.3719.
  19. Z. Zong, X. Qin, X. Ruan, K. Bellam, M. Nijim, and M. I. Alghamdi, “Energy-efficient scheduling for parallel applications running on heterogeneous clusters,” in 2007 International Conference on Parallel Processing (ICPP 2007), September 10-14, 2007, Xi-An, China. IEEE Computer Society, 2007. http://dx.doi.org/10.1109/ICPP.2007.39 p. 19. [Online].
  20. D. Li, B. R. de Supinski, M. Schulz, D. S. Nikolopoulos, and K. W. Cameron, “Strategies for energy-efficient resource management of hybrid programming models.” IEEE Trans. Parallel Distrib. Syst., vol. 24, no. 1, pp. 144–157, 2013. http://dx.doi.org/10.1109/TPDS.2012.95.
  21. P. Czarnul, Ed., Modeling Large-Scale Computing Systems. Practical Approaches in MERPSYS. Gdansk University of Technology, 2015. ISBN 978-83-938367-2-7