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A Novel Statistical Learning-Based Methodology for Measuring the Goodness of Energy Profiles of Applications Executing on Multicore Computing Platforms

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

Accurate energy profiles are essential to the optimization of parallel applications for energy through workload distribution. Since there are many model-based methods available for efficient construction of energy profiles, we need an approach to measure the goodness of the profiles compared with the ground-truth profile, which is usually built by a time-consuming but reliable method. Correlation coefficient and relative error are two such popular statistical approaches, but they assume that profiles be linear or at least very smooth functions of workload size. This assumption does not hold true in the multicore era. Due to the complex shapes of energy profiles of applications on modern multicore platforms, the statistical methods can often rank inaccurate energy profiles higher than more accurate ones and employing such profiles in the energy optimization loop of an application leads to significant energy losses (up to 54% in our case). In this work, we present the first method specifically designed for goodness measurement of energy profiles. First, it analyses the underlying energy consumption trend of each energy profile and removes the profiles that exhibit a trend different from that of the ground truth. Then, it ranks the remaining energy profiles using the Euclidean distances as a metric. We demonstrate that the proposed method is more accurate than the statistical approaches and can save a significant amount of energy.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:15:p:3944-:d:393218
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    References listed on IDEAS

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    1. Domenico Piccolo, 1990. "A Distance Measure For Classifying Arima Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 11(2), pages 153-164, March.
    2. Félix Iglesias & Wolfgang Kastner, 2013. "Analysis of Similarity Measures in Times Series Clustering for the Discovery of Building Energy Patterns," Energies, MDPI, vol. 6(2), pages 1-19, January.
    3. 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.
    4. 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.
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    Cited by:

    1. Małgorzata Poniatowska-Jaksch, 2021. "Energy Consumption in Central and Eastern Europe (CEE) Households in the Platform Economics," Energies, MDPI, vol. 14(4), pages 1-22, February.

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