Skip to main content

Advertisement

SpringerLink
Log in

Time-energy analysis of multilevel parallelism in heterogeneous clusters: the case of EEG classification in BCI tasks

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Present heterogeneous architectures interconnect nodes including multiple multi-core microprocessors and accelerators that allow different strategies to accelerate the applications and optimize their energy consumption according to the specific power-performance trade-offs. In this paper, a multilevel parallel procedure is proposed to take advantage of all nodes of a heterogeneous CPU–GPU cluster. Two more alternatives have been implemented, and experimentally compared and analyzed from both running time and energy consumption. Although the paper considers an evolutionary master–worker algorithm for feature selection in EEG classification, the conclusions from the experimental analysis here provided can be frequently applied, as many other useful bioinformatics and data mining applications show the same master–worker profile than the classification problem here considered. Our parallel approach allows to reduce the time by a factor of up to 83, with only about a 4.9% of energy consumed by the sequential procedure, in a cluster with 36 CPU cores and 43 GPU compute units.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. O’brien K, Pietri I, Reddy R, Lastovetsky A, Sakellariou R (2017) A survey of power and energy predictive models in HPC systems and applications. ACM Comput Surv 50(3):37:1–37:38

    Article  Google Scholar 

  2. Zhang Y, Hu X. Chen D (2002) Task scheduling and voltage selection for energy minimization. In: Proceedings of the 39th Annual Design Automation Conference. DAC’2002, ACM, New Orleans, Louisiana, USA, pp 183–188

  3. Baskiyar S, Abdel-Kader R (2010) Energy aware dag scheduling on heterogeneous systems. Clust Comput 13(4):373–383

    Article  Google Scholar 

  4. Lee Y, Zomaya A (2011) Energy conscious scheduling for distributed computing systems under different operating conditions. IEEE Trans Parallel Distrib Syst 22(8):1374–1381

    Article  Google Scholar 

  5. Dorronsoro B, Nesmachnow S, Taheri J, Zomaya A, Talbi EG, Bouvry P (2014) A hierarchical approach for energy-efficient scheduling of large workloads in multicore distributed systems. Sustain Comput Inform Syst 4(4):252–261

    Google Scholar 

  6. Barik R, Farooqui N, Lewis B, Hu C, Shpeisman T (2016) A black-box approach to energy-aware scheduling on integrated CPU–GPU systems. In: Proceedings of the 2016 International Symposium on Code Generation and Optimization. CGO’2016, ACM, Barcelona, Spain, pp 70–81

  7. Ortega J, Asensio-Cubero J, Gan J, Ortiz A (2016) Classification of motor imagery tasks for BCI with multiresolution analysis and multiobjective feature selection. BioMed Eng OnLine 15(1):149–164

    Google Scholar 

  8. Raju K, Niranjan N (2018) A survey on techniques for cooperative CPU–GPU computing. Sustain Comput Inform Syst 19:72–85

    Google Scholar 

  9. Mittal S, Vetter J (2014) A survey of methods for analyzing and improving GPU energy efficiency. ACM Comput Surv 47(2):19:1–19:23

    Article  Google Scholar 

  10. Escobar J, Ortega J, Díaz A, González J, Damas M (2018) Speedup and energy analysis of EEG classification for BCI tasks on CPU–GPU clusters. In: Proceedings of the 6th International Workshop on Parallelism in Bioinformatics. PBIO’2018, ACM, Barcelona, Spain, pp 33–43

  11. Vidal P, Alba E, Luna F (2017) Solving optimization problems using a hybrid systolic search on GPU plus CPU. Soft Comput 21(12):3227–3245

    Article  Google Scholar 

  12. Luong T, Melab N, Talbi E.G (July 2010) gPU-based island model for evolutionary algorithms. In: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation. GECCO’2010, ACM, Portland, OR, USA, pp 1089–1096

  13. Pospichal P, Jaros J, Schwarz J (2010) Parallel genetic algorithm on the cuda architecture. In: Proceedings of the 13th European Conference on the Applications of Evolutionary Computation. EvoApplications’2010, Springer, Istambul, Turkey, pp 442–451

  14. Sharma D, Collet P (2013) Implementation techniques for massively parallel multi-objective optimization. In: Tsutsui S, Collet P (eds) Massively parallel evolutionary computation on GPGPUs. Natural computing series. Springer, Berlin, pp 267–286

    Chapter  Google Scholar 

  15. Wong M, Cui G (2013) Data mining using parallel multi-objective evolutionary algorithms on graphics processing units. In: Tsutsui S, Collet P (eds) Massively parallel evolutionary computation on GPGPUs. Natural computing series. Springer, Berlin, pp 287–307

    Chapter  Google Scholar 

  16. Gainaru A, Slusanschi E, Trausan-Matu S (2011) Mapping data mining algorithms on a gpu architecture: a study. In: Proceedings of the 19th International Symposium. Foundations of Intelligent Systems. ISMIS’2011, Springer, Warsaw, Poland, pp 102–112

  17. Coello Coello C, Sierra M (2004) A study of the parallelization of a coevolutionary multi-objective evolutionary algorithm. In: Proceedings of the 3rd Mexican International Conference on Artificial Intelligence. MICAI’2004, Springer, Mexico City, Mexico, pp 688–697

  18. Pruhs K, Stee R, Uthaisombut P (2008) Speed scaling of tasks with precedence constraints. Theory Comput Syst 43(1):67–80

    Article  MathSciNet  MATH  Google Scholar 

  19. Rotem E, Weiser U, Mendelson A, Ginosar R, Weissmann E, Aizik Y (2016) H-earth: heterogeneous multicore platform energy management. IEEE Comput Mag 49(10):47–55

    Article  Google Scholar 

  20. Nesmachnow S, Dorronsoro B, Pecero J, Bouvry P (2013) Energy-aware scheduling on multicore heterogeneous grid computing systems. J Grid Comput 11(4):653–680

    Article  Google Scholar 

  21. Valentini G, Lassonde W, Khan S, Min-Allah N, Madani S, Li J, Zhang L, Wang L, Ghani N, Kolodziej J, Li H, Zomaya A, Xu CZ, Balaji P, Vishnu A, Pinel F, Pecero J, Kliazovich D, Bouvry P (2013) An overview of energy efficiency techniques in cluster computing systems. Clust Comput 16(1):3–15

    Article  Google Scholar 

  22. Hong S, Kim H (2010) An integrated GPU power and performance model. SIGARCH Comput Arch News 38(3):280–289

    Article  Google Scholar 

  23. Ge R, Feng X, Burtscher M, Zong Z (2014) Peach: a model for performance and energy aware cooperative hybrid computing. In: Proceedings of the 11th ACM Conference on Computing Frontiers. CF’2014, ACM, Cagliari, Italy, pp 24:1–24:2

  24. De Sensi D (2016) Predicting performance and power consumption of parallel applications. In: Proceedings of the 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing. PDP’2016, IEEE, Heraklion Crete, Greece, pp 200–207

  25. Marowka A (2012) Energy consumption modeling for hybrid computing. In: Proceedings of the 18th International Conference on Parallel Processing, Euro-Par 2012. Euro-Par’2012, Springer, Rhodes Island, Greece, pp 54–64

  26. Ma K, Li X, Chen W, Zhang C, Wang X (2012) Greengpu: a holistic approach to energy efficiency in GPU–CPU heterogeneous architectures. In: Proceedings of the 41st International Conference on Parallel Processing. ICPP’2012, IEEE, Pittsburgh, PA, USA, pp 48–57

  27. Allen T, Ge R (2016) Characterizing power and performance of GPU memory access. In: Proceedings of the 4th International Workshop on Energy Efficient Supercomputing. E2SC’2016, IEEE Press, Salt Lake City, Utah, USA, pp 46–53

  28. Escobar J, Ortega J, Díaz A, González J, Damas M (2018) Energy-aware load balancing of parallel evolutionary algorithms with heavy fitness functions in heterogeneous CPU–GPU architectures. Concurrency and Computation: Practice and Experience, p e4688

  29. Free Software Foundation: GNU gprof documentation. https://ftp.gnu.org/pub/old-gnu/Manuals/gprof-2.9.1/html_node/gprof_toc.html. Accessed 10 Feb 2017

  30. Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182

    MATH  Google Scholar 

  31. Charikar M, Guruswami V, Kumar R, Rajagopalan S, Sahai A (2000) Combinatorial feature selection problems. In: Proceedings of the 41st Annual Symposium on Foundations of Computer Science. FOCS’2000, IEEE, Redondo Beach, CA, USA, pp 631–640

  32. Khronos Group: Khronos opencl registry (2015). https://www.khronos.org/registry/cl/. Accessed 30 Nov 2015

  33. OpenMP Community: Openmp specifications. http://www.openmp.org/specifications/. Accessed 21 Nov 2016

  34. Escobar J, Ortega J, Díaz A, González J, Damas M (2018) Multi-objective feature selection for eeg classification with multi-level parallelism on heterogeneous CPU–GPU clusters. In: Proceedings of the Annual Conference on Genetic and Evolutionary Computation. GECCO’2018, ACM, Kyoto, Japan, pp 1862–1869

  35. The Open MPI Project: Openmpi documentation. https://www.open-mpi.org/doc/. Accessed 19 Nov 2018

  36. Escobar J, Ortega J, González J, Damas M (2016) Assessing parallel heterogeneous computer architectures for multiobjective feature selection on EEG classification. In: Proceedings of the 4th International Conference on Bioinformatics and Biomedical Engineering. IWBBIO’2016, Springer, Granada, Spain, pp 277–289

  37. Escobar J, Ortega J, González J, Damas M (2016) Improving memory accesses for heterogeneous parallel multi-objective feature selection on EEG classification. In: Proceedings of the 4th International Workshop on Parallelism in Bioinformatics. PBIO’2016, Springer, Grenoble, France, pp 372–383

  38. Escobar J, Ortega J, González J, Damas M, Díaz A (2017) Parallel high-dimensional multi-objective feature selection for EEG classification with dynamic workload balancing on CPU–GPU. Clust Comput 20(3):1881–1897

    Article  Google Scholar 

  39. Asensio-Cubero J, Gan J, Palaniappan R (2013) Multiresolution analysis over simple graphs for brain computer interfaces. J Neural Eng 10(4):21–26

    Article  Google Scholar 

  40. Zitzler E, Thiele L (1998) Multiobjective optimization using evolutionary algorithms—a comparative case study. In: Proceedings of the 5th International Conference on Parallel Problem Solving from Nature. PPSN V, Springer, Amsterdam, The Netherlands, pp 292–301

  41. Zitzler E (1999) Evolutionary algorithms for multiobjective optimization: methods and applications. Shaker Verlag, Herzogenrath

    Google Scholar 

  42. Sîrbu A, Babaoglu O (2016) Power consumption modeling and prediction in a hybrid CPU–GPU–MIC supercomputer. In: Proceedings of the 22nd International Conference on Parallel Processing, Euro-Par 2016. Euro-Par’2016, Springer, Grenoble, France, pp 117–130

  43. Advanced Configuration and Power Interface (ACPI): Acpi specification. http://www.acpi.info/spec.htm. Accessed 30 Nov 2018

  44. CPUFreq Governors: information for users and developers. https://www.kernel.org/doc/Documentation/cpu-freq/governors.txt. Accessed 30 Nov 2018

  45. Mathworks: Matlab histfit function. https://mathworks.com/help/stats/histfit.html. Accessed 02 Dec 2018

Download references

Acknowledgements

We would like to thank the BCI laboratory of the University of Essex, especially prof. John Q. Gan, for allowing us to use their databases

Author information

Authors and Affiliations

  1. Department of Computer Architecture and Technology, CITIC, University of Granada, Granada, Spain

    Juan José Escobar, Julio Ortega, Antonio F. Díaz, Jesús González & Miguel Damas

Authors
  1. Juan José Escobar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Julio Ortega
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Antonio F. Díaz
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jesús González
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Miguel Damas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Juan José Escobar.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This research has been funded by the Spanish “Ministerio de Ciencia, Innovación y Universidades” through the Grant PGC2018-098813-B-C31 and the ERDF funds.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Escobar, J.J., Ortega, J., Díaz, A.F. et al. Time-energy analysis of multilevel parallelism in heterogeneous clusters: the case of EEG classification in BCI tasks. J Supercomput 75, 3397–3425 (2019). https://doi.org/10.1007/s11227-019-02908-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-019-02908-4

Keywords

Search

Navigation