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

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.