Skip to main content
SpringerLink
Log in
Book cover

Harnessing Performance Variability in Embedded and High-performance Many/Multi-core Platforms pp 265–293Cite as

Floreon+ Modules: A Real-World HARPA Application in the High-End HPC System Domain

  • Chapter
  • First Online:

  • 456 Accesses

Abstract

This chapter is centered around uncertainty computation with on-demand resource allocation for run-off prediction in a High-Performance Computer environment. Our research stands on a runtime operating system that automatically adapts resource allocation with the computation to provide precise outcomes before the time deadline. In our case, input data comes from several gauging stations, and when newly updated data arrives, models must be re-executed to provide accurate results immediately. Since the models run continuously (24/7), their computational demand is different during various hydrological events (e.g. periods with heavy rain and without any rain) and therefore computational resources have to be balanced according to the event severity. Although these kinds of models should run constantly, they are very computationally demanding during discrete periods of time, for example in the case of heavy rain. Then, the accuracy of the results must be as close as possible to reality. The work relies on the HARPA runtime resource manager that adapts resource allocation to the runtime-variable performance demand of applications. The resource assignment is temperature-aware: the application execution is dynamically migrated to the coolest cores, and this has a positive impact on the system reliability.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Abulohom, M. S., Shah, M. S., & Ghumman, A. R. (2001). Development of a rainfall-runoff model, its calibration and validation. Water Resources Management, 15(3), 149163.

    Article  Google Scholar 

  2. Bahremand, A., & De Smedt, F. (2008). Distributed hydrological modeling and sensitivity analysis in Torysa watershed, Slovakia. Water Resources Management, 22(3), 293408.

    Article  Google Scholar 

  3. Bellas, P., Massari, G., & Fornaciari, W. (2012). A RTMR proposal for multi/many-core platforms and reconfigurable applications. In ReCoSoC.

    Google Scholar 

  4. Bellasi P., Massari, G., & Fornaciari, W. (2015). Effective runtime resource management using linux control groups with the barbequertrm framework. ACM Transactions on Embedded Computing Systems, 14(2), 39:139:17.

    Article  Google Scholar 

  5. Beven, K. (2012). Rainfall-runoff modelling. New York: John Wiley & Sons.

    Book  Google Scholar 

  6. Billinton, R., Allan, R.N., IEEE Power Engineering Society. Power Engineering Education Committee, & IEEE Power Engineering Society. Power system Engineering Committee (1989). Reliability assessment of composite generation and transmission systems. IEEE Power Engineering Society Tutorial, 90EH0311-1-PWR.

    Google Scholar 

  7. Billinton, R., & Li, W. (1994). Reliability assessment of electric power systems using Monte Carlo methods. New York: Plenum Press.

    Book  Google Scholar 

  8. Bolchini, C., Carminati, M., Gribaudo, M., & Miele, A. (2014). A lightweight and open-source framework for the lifetime estimation of multicore systems. In IEEE 32nd International Conference on Computer Design (ICCD).

    Google Scholar 

  9. Ellerman, P. (2012). Calculating reliability using FIT & MTTF: Arrhenius HTOL model. In MICROSEMI, Technical Report.

    Google Scholar 

  10. Ganeshpure, K., & Kundu, S. (2014). Performance-driven dynamic thermal management of mpsoc based on task rescheduling. ACM Transactions on Design Automation of Electronic Systems, 19(2), 11:1–11:33.

    Article  Google Scholar 

  11. Hardy, D., Sideris, I., Ladas, N., & Sazeides, Y. (2012). The performance vulnerability of architectural and non-architectural arrays to permanent faults. In MICRO (pp. 48–59).

    Google Scholar 

  12. Harpa harnessing performance variability fp7 project (2013). http://www.harpa-project.eu

  13. Hartman, A. S., & Thomas, D. E. (2012). Lifetime improvement through runtime wear-based task mapping. In Proceedings of the Eighth IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis, CODES+ISSS’12 (pp. 13–22). New York: ACM.

    Chapter  Google Scholar 

  14. Golasowski, M., Litschmannova, M., Kuchar, M., Podhoranyi, M., & Martinovic, J. (2015). Uncertainty modelling in rainfall-runoff simulations based on parallel Monte Carlo method. International Journal on Non-standard Computing and Artificial Intelligence NNW, 25(3), 267–286.

    Google Scholar 

  15. Martinovic, J., Kuchar, S., Vondrak, I., Vondrak, V., Sir, B., & Unucka, J. (2010). Multiple scenarios computing in the flood prediction system FLOREON. In European Conference on Modelling and Simulation (pp. 182–188).

    Google Scholar 

  16. Massari, G., Libutti, S., Portero, A., Vavrik, R., Vondrak, V., & Fornaciari, W. (2015). Harnessing performance variability: A HPC-oriented application scenario. In Euromicro Conference on Digital System Design (DSD), Funchal, Madeira, Portugal.

    Google Scholar 

  17. Meesuka, V., Vojinovica, Z., Mynetta, A. E., & Abdullahd, A. F. (2015). Urban flood modelling combining top-view LiDAR data with ground-view SfM observations. Advances in Water Resources, 75, 105–117.

    Article  Google Scholar 

  18. Nash, J., & Sutcliffe, J. (1970). River flow forecasting through conceptual models part I. A discussion of principles. Journal of Hydrology, 10(3), 282290. https://www.doi.org/10.1016/0022-1694(70)90255-6. Procedia Computer Science

    Article  Google Scholar 

  19. National Semiconductor. (2000). Understanding integrated circuit package power capabilities. Available: www.national.com

    Google Scholar 

  20. Oh, D., Kim, N. S., Chen, C. C. P., Davoodi, A., & Hu, Y. H. (2010). Runtime temperature-based power estimation for optimizing throughput of thermal-constrained multi-core processors. In Proceedings of the 2010 Asia and South Pacific Design Automation Conference, ASPDAC‘10, Piscataway (pp. 593–599). New York: IEEE Press.

    Google Scholar 

  21. Portero, A., Kuchar, S., Vavrik, R., Golasowski, M., & Vondrak, V. (2014). System and application scenarios for disaster management processes, the rainfall-runoff model case study. In CISIM (pp. 315–326).

    Chapter  Google Scholar 

  22. Portero, A., Sevcík, J., Golasowski, M., Vavrík, R., Libutti, S., Massari, G., et al. (2016). Using an adaptive and time predictable runtime system for power-aware HPC-oriented applications. In IGSC 2016 (pp. 1–6).

    Google Scholar 

  23. Powell, M. D., Gomaa, M., & Vijaykumar, T. N. (2004). Heat-and-run: leveraging SMT and CMP to manage power density through the operating system. In ASPLOS.

    Google Scholar 

  24. Qiua, L., Dua, Z., Zhua, Q., & Fane, Y. (2017). An integrated flood management system based on linking environmental models and disaster-related data. Environmental Modelling & Software, 91, 111–126.

    Article  Google Scholar 

  25. Sacerdoti, F. D., Katz, M. J., & Massie, M. L. & Culler, D. E. (2003). Wide area cluster monitoring with Ganglia. In CLUSTER (Vol. 3, pp. 289–289).

    Google Scholar 

  26. Treibig, J., Hager, G., & Wellein, G. (2010). Likwid: A lightweight performance-oriented tool suite for x86 multicore environments. In Proceedings of the 39th International Conference on Parallel Processing Workshops (ICPPW) (pp. 207–216). New York: IEEE.

    Google Scholar 

  27. Vavrik, R., Theuer, M., Golasowski, M., Kuchar, S., Podhoranyi, M., & Vondrak, V. (2015). Automatic calibration of rainfall-runoff models and its parallelization strategies. AIP Conference Proceedings, 1648, 830014. https://aip.scitation.org/doi/abs/10.1063/1.4913040

    Article  Google Scholar 

  28. Yoshimoto, K. K., Choi, D. J., Moore, R. L., Majumdar, A., & Hocks, E. (2012). Implementations of urgent computing on production HPC systems. Procedia Computer Science, 9, 1687–1693.

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by The Ministry of Education, Youth and Sports from the National Programme of Sustainability (NPU II) project “IT4Innovations excellence in science—LQ1602” and by the IT4Innovations infrastructure which is supported from the Large Infrastructures for Research, Experimental Development and Innovations project “IT4Innovations National Supercomputing Center LM2015070”. The work was also supported by the European Union FP-7 program through the HARPA project (grant no. 612069).

Author information

Authors and Affiliations

  1. IT4Innovations, Ostrava - Poruba, Czech Republic

    Antoni Portero, Radim Vavrik, Martin Golasowski, Jiri Sevcik, Stepan Kuchar & Vit Vondrak

  2. Politecnico di Milano, Milano, Italy

    Giuseppe Massari, Simone Libutti & William Fornaciari

Authors
  1. Antoni Portero
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Radim Vavrik
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Martin Golasowski
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jiri Sevcik
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Giuseppe Massari
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Simone Libutti
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. William Fornaciari
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Stepan Kuchar
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Vit Vondrak
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Antoni Portero .

Editor information

Editors and Affiliations

  1. DEIB – Dipartimento di Elettronica Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy

    William Fornaciari

  2. School of Electrical & Comp. Engineering, Natl. Technical Univ. of Athens, Athens, Greece

    Dimitrios Soudris

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Portero, A. et al. (2019). Floreon+ Modules: A Real-World HARPA Application in the High-End HPC System Domain. In: Fornaciari, W., Soudris, D. (eds) Harnessing Performance Variability in Embedded and High-performance Many/Multi-core Platforms. Springer, Cham. https://doi.org/10.1007/978-3-319-91962-1_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-91962-1_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-91961-4

  • Online ISBN: 978-3-319-91962-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation