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Analyzing the energy consumption of the storage data path

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Abstract

Data movement is a key aspect of energy consumption in modern computing systems. As computation becomes more energy efficient, the cost of data movement gradually becomes a more relevant issue, especially in high-performance computing systems. The relevance of data movement can be studied at different scales, ranging from microcontrollers and microarchitectures to future Exascale systems. The goal of this work is to analyze the power costs of performing I/O operations and intra-node data movement, focusing on the operating system’s I/O stack. Our approach combines the hardware instrumentation, software instrumentation, and data analysis techniques to gain insights into how different I/O patterns make use of system resources, including electrical power. We synthesize this data-driven process into a methodology and present the results of applying this methodology on sequential read and write patterns. As a result, we identify the key system metrics that contribute to I/O-related power usage and discover how the system makes transitions between different power and performance regimes based on the I/O patterns.

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Notes

  1. Note that the absolute value of the derivative is computed to superimpose positive and negative rates on a single normalized plot.

  2. Power proportionality is a metric that measures the degree to which the power used by a system is proportional to the required utilization [35].

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Acknowledgments

The work presented in this paper has been partially supported by the EU Project FP7 318793 “EXA2GREEN” and partially supported by the EU under the COST Programme Action IC1305 “Network for Sustainable Ultrascale Computing (NESUS)” and by the Grant TIN2013-41350-P, Scalable Data Management Techniques for High-End Computing Systems from the Spanish Ministry of Economy and Competitiveness.

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Authors and Affiliations

  1. Computer Science and Engineering Department, Universidad Carlos III, 28911, Leganes, Madrid, Spain

    Pablo Llopis, Manuel F. Dolz, Javier Garcia Blas & Florin Isaila

  2. Department of Informatics, University of Hamburg, 20146, Hamburg, Germany

    Mohammad Reza Heidari & Michael Kuhn

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  1. Pablo Llopis
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  2. Manuel F. Dolz
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  3. Javier Garcia Blas
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Correspondence to Pablo Llopis.

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Llopis, P., Dolz, M.F., Blas, J.G. et al. Analyzing the energy consumption of the storage data path. J Supercomput 72, 4089–4106 (2016). https://doi.org/10.1007/s11227-016-1729-4

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