Evaluating Scientific Workflow Execution on an Asymmetric Multicore Processor

  • Ilia Pietri
  • Sicong Zhuang
  • Marc Casas
  • Miquel Moretó
  • Rizos Sakellariou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10659)


Asymmetric multicore architectures that integrate different types of cores are emerging as a potential solution for good performance and power efficiency. Although scheduling can be improved by utilizing an appropriate set of cores for the execution of the different jobs, determining frequency configurations is also crucial to achieve both good performance and energy efficiency. This challenge may be more profound with scientific workflow applications that consist of jobs with data dependency constraints. The paper focuses on deploying and evaluating the Montage scientific workflow on an asymmetric multicore platform with the aim to explore CPU frequency configurations with different trade-offs between execution time and energy efficiency. The proposed approach provides good estimates of workflow execution time and energy consumption for different frequency configurations with an average error of less than 8.63% for time and less than 9.69% for energy compared to actual values.



This work was supported through a collaboration grant from HiPEAC (, the RoMoL ERC Advanced Grant (GA 321253), by the Spanish Ministry of Science and Innovation (contract TIN2015-65316-P), and by Generalitat de Catalunya (contracts 2014-SGR-1051 and 2014-SGR-1272).


  1. 1.
    Ahmad, I., Ranka, S., Khan, S.U.: Using game theory for scheduling tasks on multi-core processors for simultaneous optimization of performance and energy. In: Proceedings of the IPDPS, pp. 1–6. IEEE (2008)Google Scholar
  2. 2.
    Canon, L.C., Jeannot, E., Sakellariou, R., Zheng, W.: Comparative evaluation of the robustness of DAG scheduling heuristics. In: Gorlatch, S., Fragopoulou, P., Priol, T. (eds.) Grid Computing: Achievements and Prospects, pp. 73–84. Springer, Boston (2008). CrossRefGoogle Scholar
  3. 3.
    Chronaki, K., Rico, A., Casas, M., Moreto, M., Badia, R., Ayguade, E., Labarta, J., Valero, M.: Task scheduling techniques for asymmetric multi-core systems. IEEE Trans. Parallel Distrib. Syst. 28(7), 2074–2087 (2017)CrossRefGoogle Scholar
  4. 4.
    Deelman, E., Gannon, D., Shields, M., Taylor, I.: Workflows and e-Science: an overview of workflow system features and capabilities. Future Gener. Comput. Syst. 25(5), 528–540 (2009)CrossRefGoogle Scholar
  5. 5.
    Deelman, E., Singh, G., Su, M.H., Blythe, J., Gil, Y., Kesselman, C., Mehta, G., Vahi, K., Berriman, G.B., Good, J.: Pegasus: a framework for mapping complex scientific workflows onto distributed systems. Sci. Program. 13(3), 219–237 (2005)Google Scholar
  6. 6.
    Durillo, J.J., Nae, V., Prodan, R.: Multi-objective energy-efficient workflow scheduling using list-based heuristics. Future Gener. Comput. Syst. 36, 221–236 (2014)CrossRefGoogle Scholar
  7. 7.
    Etinski, M., Corbalan, J., Labarta, J., Valero, M.: Understanding the future of energy-performance trade-off via DVFS in HPC environments. J. Parallel Distrib. Comput. 72(4), 579–590 (2012)CrossRefGoogle Scholar
  8. 8.
    Ge, R., Feng, X., Cameron, K.W.: Modeling and evaluating energy-performance efficiency of parallel processing on multicore based power aware systems. In: Proceedings of the IPDPS, pp. 1–8. IEEE (2009)Google Scholar
  9. 9.
    Katz, D.S., Jacob, J.C., Deelman, E., Kesselman, C., Singh, G., Su, M.H., Berriman, G., Good, J., Laity, A., Prince, T.A.: A comparison of two methods for building astronomical image mosaics on a grid. In: Proceedings of the IEEE International Conference on Parallel Processing Workshops, pp. 85–94. IEEE (2005)Google Scholar
  10. 10.
    Kumar, R., Tullsen, D.M., Ranganathan, P., Jouppi, N.P., Farkas, K.I.: Single-ISA heterogeneous multi-core architectures for multithreaded workload performance. ACM SIGARCH Comput. Archit. News 32(2), 64–75 (2004)CrossRefGoogle Scholar
  11. 11.
    Li, K., Tang, X., Veeravalli, B., Li, K.: Scheduling precedence constrained stochastic tasks on heterogeneous cluster systems. IEEE TC 64(1), 191–204 (2015)MathSciNetzbMATHGoogle Scholar
  12. 12.
    Topcuoglu, H., Hariri, S., Wu, M.Y.: Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE TPDS 13(3), 260–274 (2002)Google Scholar
  13. 13.
    Van Craeynest, K., Jaleel, A., Eeckhout, L., Narvaez, P., Emer, J.: Scheduling heterogeneous multi-cores through performance impact estimation (PIE). ACM SIGARCH Comput. Archit. News 40(3), 213–224 (2012)CrossRefGoogle Scholar
  14. 14.
    Von Laszewski, G., Wang, L., Younge, A.J., He, X.: Power-aware scheduling of virtual machines in DVFS-enabled clusters. In: Proceedings of the IEEE International Conference on Cluster Computing and Workshops, pp. 1–10. IEEE (2009)Google Scholar
  15. 15.
    Young, B.D., Pasricha, S., Maciejewski, A.A., Siegel, H.J., Smith, J.T.: Heterogeneous makespan and energy-constrained DAG scheduling. In: Proceedings of the Workshop on Energy Efficient High Performance Parallel and Distributed Computing, pp. 3–12. ACM (2013)Google Scholar
  16. 16.
    Zheng, W., Bao, W., Xu, C., Zhang, D.: Evaluation of the DAG ready tasks maximization algorithms in multi-core computing platforms. In: Proceedings of the CBD, pp. 110–115 (2016)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Informatics and TelecommunicationsUniversity of AthensAthensGreece
  2. 2.Barcelona Supercomputing Center (BSC)BarcelonaSpain
  3. 3.Universitat Politecnica de Catalunya (UPC)BarcelonaSpain
  4. 4.School of Computer ScienceUniversity of ManchesterManchesterUK

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