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    Run-time Predictive Modeling of Power and Performance via Time-Series in High Performance Computing

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    Date
    2012-11-13
    Author
    Zamani, Reza
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    Abstract
    Pressing demands for less power consumption of processors while delivering higher performance

    levels have put an extra attention on efficiency of the systems. Efficient management

    of resources in the current computing systems, given their increasing number of entities and

    complexity, requires accurate predictive models that can easily adapt to system and application

    changes. Through performance monitoring counter (PMC) events, in modern processors, a vast

    amount of information can be obtained from the system. This thesis provides a methodology

    to efficiently choose such events for power modeling purposes. In addition, exploiting the

    time-dependence of the data measured through PMCs and multi-meters, we build predictive

    multivariate time-series models that estimate the run-time power consumption of a system. In

    particular, we find an autoregressive moving average with exogenous inputs (ARMAX) model

    that is combined with a recursive least squares (RLS) algorithm as a good candidate for such

    purposes.

    Many of the available estimation or prediction models avoid using the metrics that are

    affected by the changes of the processor frequency. This thesis proposes a method to mitigate

    the impact of frequency scaling in a run-time model on power and PMC metrics. This method is

    based on a practical Gaussian approximation. Different segments of the trend of a metric that

    are associated with different frequencies are scaled and offset into a zero mean unit variance

    signal. This is an attempt to transform the variable frequency trend into a weakly stationary

    time-series. Using this approach, we have shown that power estimation of a system using PMCs

    can be done in a variable frequency environment.

    We extend the ARMAX-RLS model to predict the near future power consumption and

    PMCs of different applications in a variable frequency environment. The proposed method is

    adaptive, independent of the system and applications. We have shown that a run-time per core

    or aggregate system PMC event prediction, multiple-steps ahead of time, is feasible using an

    ARMAX-RLS model. This is crucial for progressing from the reactive power and performance

    management methods to more proactive algorithms.
    URI for this record
    http://hdl.handle.net/1974/7639
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    • Department of Electrical and Computer Engineering Graduate Theses
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