IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i11p2204-d238543.html
   My bibliography  Save this article

A Comparative Study of Methods for Measurement of Energy of Computing

Author

Listed:
  • Muhammad Fahad

    (School of Computer Science, University College Dublin, Belfield, Dublin-4, Ireland)

  • Arsalan Shahid

    (School of Computer Science, University College Dublin, Belfield, Dublin-4, Ireland)

  • Ravi Reddy Manumachu

    (School of Computer Science, University College Dublin, Belfield, Dublin-4, Ireland)

  • Alexey Lastovetsky

    (School of Computer Science, University College Dublin, Belfield, Dublin-4, Ireland)

Abstract

Energy of computing is a serious environmental concern and mitigating it is an important technological challenge. Accurate measurement of energy consumption during an application execution is key to application-level energy minimization techniques. There are three popular approaches to providing it: (a) System-level physical measurements using external power meters; (b) Measurements using on-chip power sensors and (c) Energy predictive models. In this work, we present a comprehensive study comparing the accuracy of state-of-the-art on-chip power sensors and energy predictive models against system-level physical measurements using external power meters, which we consider to be the ground truth. We show that the average error of the dynamic energy profiles obtained using on-chip power sensors can be as high as 73% and the maximum reaches 300% for two scientific applications, matrix-matrix multiplication and 2D fast Fourier transform for a wide range of problem sizes. The applications are executed on three modern Intel multicore CPUs, two Nvidia GPUs and an Intel Xeon Phi accelerator. The average error of the energy predictive models employing performance monitoring counters (PMCs) as predictor variables can be as high as 32% and the maximum reaches 100% for a diverse set of seventeen benchmarks executed on two Intel multicore CPUs (one Haswell and the other Skylake). We also demonstrate that using inaccurate energy measurements provided by on-chip sensors for dynamic energy optimization can result in significant energy losses up to 84%. We show that, owing to the nature of the deviations of the energy measurements provided by on-chip sensors from the ground truth, calibration can not improve the accuracy of the on-chip sensors to an extent that can allow them to be used in optimization of applications for dynamic energy. Finally, we present the lessons learned, our recommendations for the use of on-chip sensors and energy predictive models and future directions.

Suggested Citation

  • Muhammad Fahad & Arsalan Shahid & Ravi Reddy Manumachu & Alexey Lastovetsky, 2019. "A Comparative Study of Methods for Measurement of Energy of Computing," Energies, MDPI, vol. 12(11), pages 1-42, June.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:11:p:2204-:d:238543
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/11/2204/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/12/11/2204/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nicola Jones, 2018. "How to stop data centres from gobbling up the world’s electricity," Nature, Nature, vol. 561(7722), pages 163-166, September.
    2. Anders S. G. Andrae & Tomas Edler, 2015. "On Global Electricity Usage of Communication Technology: Trends to 2030," Challenges, MDPI, vol. 6(1), pages 1-41, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Muhammad Fahad & Arsalan Shahid & Ravi Reddy Manumachu & Alexey Lastovetsky, 2020. "A Novel Statistical Learning-Based Methodology for Measuring the Goodness of Energy Profiles of Applications Executing on Multicore Computing Platforms," Energies, MDPI, vol. 13(15), pages 1-22, August.
    2. Adrian Kampa & Iwona Paprocka, 2021. "Analysis of Energy Efficient Scheduling of the Manufacturing Line with Finite Buffer Capacity and Machine Setup and Shutdown Times," Energies, MDPI, vol. 14(21), pages 1-25, November.
    3. Stanly Jayaprakash & Manikanda Devarajan Nagarajan & Rocío Pérez de Prado & Sugumaran Subramanian & Parameshachari Bidare Divakarachari, 2021. "A Systematic Review of Energy Management Strategies for Resource Allocation in the Cloud: Clustering, Optimization and Machine Learning," Energies, MDPI, vol. 14(17), pages 1-18, August.
    4. Alberto Ortega & Abel Miguel Cano-Delgado & Beatriz Prieto & Jesús González, 2023. "Design of a Standard and Programmatically Accessible Interface for Smart Meters to Allow Monitoring Automation of the Energy Consumed by the Execution of Computer Software," Sustainability, MDPI, vol. 15(3), pages 1-16, January.
    5. Bartłomiej Kocot & Paweł Czarnul & Jerzy Proficz, 2023. "Energy-Aware Scheduling for High-Performance Computing Systems: A Survey," Energies, MDPI, vol. 16(2), pages 1-28, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. John Martinovic & Markus Hähnel & Guntram Scheithauer & Waltenegus Dargie, 2022. "An introduction to stochastic bin packing-based server consolidation with conflicts," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(2), pages 296-331, July.
    2. Salil Bharany & Sandeep Sharma & Osamah Ibrahim Khalaf & Ghaida Muttashar Abdulsahib & Abeer S. Al Humaimeedy & Theyazn H. H. Aldhyani & Mashael Maashi & Hasan Alkahtani, 2022. "A Systematic Survey on Energy-Efficient Techniques in Sustainable Cloud Computing," Sustainability, MDPI, vol. 14(10), pages 1-89, May.
    3. Khokhriakov, Semyon & Manumachu, Ravi Reddy & Lastovetsky, Alexey, 2020. "Multicore processor computing is not energy proportional: An opportunity for bi-objective optimization for energy and performance," Applied Energy, Elsevier, vol. 268(C).
    4. Bourgeois Guillaume & Duthil Benjamin & Courboulay Vincent, 2022. "Review of the Impact of IT on the Environment and Solution with a Detailed Assessment of the Associated Gray Literature," Sustainability, MDPI, vol. 14(4), pages 1-19, February.
    5. Matteo Manganelli & Alessandro Soldati & Luigi Martirano & Seeram Ramakrishna, 2021. "Strategies for Improving the Sustainability of Data Centers via Energy Mix, Energy Conservation, and Circular Energy," Sustainability, MDPI, vol. 13(11), pages 1-25, May.
    6. Li, Xiang & Lepour, Dorsan & Heymann, Fabian & Maréchal, François, 2023. "Electrification and digitalization effects on sectoral energy demand and consumption: A prospective study towards 2050," Energy, Elsevier, vol. 279(C).
    7. Robert Basmadjian, 2019. "Flexibility-Based Energy and Demand Management in Data Centers: A Case Study for Cloud Computing," Energies, MDPI, vol. 12(17), pages 1-22, August.
    8. Zhenxiang Cao & Liqing Peng, 2023. "The Impact of Digital Economics on Environmental Quality: A System Dynamics Approach," SAGE Open, , vol. 13(4), pages 21582440231, December.
    9. Steffen Dalsgaard, 2022. "Can IT Resolve the Climate Crisis? Sketching the Role of an Anthropology of Digital Technology," Sustainability, MDPI, vol. 14(10), pages 1-17, May.
    10. Axenbeck, Janna & Niebel, Thomas, 2021. "Climate Protection Potentials of Digitalized Production Processes: Microeconometric Evidence," 23rd ITS Biennial Conference, Online Conference / Gothenburg 2021. Digital societies and industrial transformations: Policies, markets, and technologies in a post-Covid world 238007, International Telecommunications Society (ITS).
    11. Lange, Steffen & Pohl, Johanna & Santarius, Tilman, 2020. "Digitalization and energy consumption. Does ICT reduce energy demand?," Ecological Economics, Elsevier, vol. 176(C).
    12. Ana Salomé García-Muñiz & María Rosalía Vicente, 2021. "The Effects of Informational Feedback on the Energy Consumption of Online Services: Some Evidence for the European Union," Energies, MDPI, vol. 14(10), pages 1-14, May.
    13. Fridgen, Gilbert & Keller, Robert & Körner, Marc-Fabian & Schöpf, Michael, 2020. "A holistic view on sector coupling," Energy Policy, Elsevier, vol. 147(C).
    14. Erik Champion & Hafizur Rahaman, 2019. "3D Digital Heritage Models as Sustainable Scholarly Resources," Sustainability, MDPI, vol. 11(8), pages 1-8, April.
    15. Anders S. G. Andrae & Mengjun Xia & Jianli Zhang & Xiaoming Tang, 2016. "Practical Eco-Design and Eco-Innovation of Consumer Electronics—the Case of Mobile Phones," Challenges, MDPI, vol. 7(1), pages 1-19, February.
    16. Tilman Santarius & Johanna Pohl & Steffen Lange, 2020. "Digitalization and the Decoupling Debate: Can ICT Help to Reduce Environmental Impacts While the Economy Keeps Growing?," Sustainability, MDPI, vol. 12(18), pages 1-20, September.
    17. Anders S. G. Andrae & Mikko Samuli Vaija, 2017. "Precision of a Streamlined Life Cycle Assessment Approach Used in Eco-Rating of Mobile Phones," Challenges, MDPI, vol. 8(2), pages 1-24, August.
    18. Nguyen, Quyen & Diaz-Rainey, Ivan & Kuruppuarachchi, Duminda, 2021. "Predicting corporate carbon footprints for climate finance risk analyses: A machine learning approach," Energy Economics, Elsevier, vol. 95(C).
    19. Elgaaied-Gambier, Leila & Bertrandias, Laurent & Bernard, Yohan, 2020. "Cutting the Internet's Environmental Footprint: An Analysis of Consumers' Self-Attribution of Responsibility," Journal of Interactive Marketing, Elsevier, vol. 50(C), pages 120-135.
    20. Sovacool, Benjamin K. & Martiskainen, Mari & Furszyfer Del Rio, Dylan D., 2021. "Knowledge, energy sustainability, and vulnerability in the demographics of smart home technology diffusion," Energy Policy, Elsevier, vol. 153(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:12:y:2019:i:11:p:2204-:d:238543. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.