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    I/O Aware Power Shifting

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    Lavoie_Power_Shifting.pdf
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    Description:
    Final Accepted Manuscript
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    Author
    Savoie, Lee
    Lowenthal, David K.
    Supinski, Bronis R. de
    Islam, Tanzima
    Mohror, Kathryn
    Rountree, Barry
    Schulz, Martin
    Affiliation
    Dept. of Comput. Sci., Univ. of Arizona
    Issue Date
    2016-05
    Keywords
    parallel processing
    power aware computing
    I/O aware power shifting
    fixed power bound
    high-performance computing
    phase frequency
    Algorithm design and analysis
    Clustering algorithms
    Data visualization
    Delays
    Heuristic algorithms
    Schedules
    HPC
    Performance
    Power
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    Metadata
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    Publisher
    IEEE
    Citation
    L. Savoie et al., "I/O Aware Power Shifting," 2016 IEEE International Parallel and Distributed Processing Symposium (IPDPS), Chicago, IL, 2016, pp. 740-749.
    Journal
    2016 IEEE 30TH INTERNATIONAL PARALLEL AND DISTRIBUTED PROCESSING SYMPOSIUM (IPDPS 2016)
    Rights
    Copyright © 2016, IEEE.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    Power limits on future high-performance computing (HPC) systems will constrain applications. However, HPC applications do not consume constant power over their lifetimes. Thus, applications assigned a fixed power bound may be forced to slow down during high-power computation phases, but may not consume their full power allocation during low-power I/O phases. This paper explores algorithms that leverage application semantics-phase frequency, duration and power needs-to shift unused power from applications in I/O phases to applications in computation phases, thus improving system-wide performance. We design novel techniques that include explicit staggering of applications to improve power shifting. Compared to executing without power shifting, our algorithms can improve average performance by up to 8% or improve performance of a single, high-priority application by up to 32%.
    Note
    No embargo.
    DOI
    10.1109/IPDPS.2016.15
    Version
    Final accepted manuscript
    Additional Links
    http://ieeexplore.ieee.org/document/7516070/
    ae974a485f413a2113503eed53cd6c53
    10.1109/IPDPS.2016.15
    Scopus Count
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    UA Faculty Publications

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