Predicting Computer Performance Dynamics

  • Joshua Garland
  • Elizabeth Bradley
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7014)

Abstract

Traditional approaches to the design and analysis of computer systems employ linear, stochastic mathematics—techniques that are becoming increasingly inadequate as computer architects push the design envelope. To work effectively with these complex engineered systems, one needs models that correctly capture their dynamics, which are deterministic and highly nonlinear. This is important not only for analysis, but also for design. Even an approximate forecast of the state variables of a running computer could be very useful in tailoring system resources on the fly to the dynamics of a computing application—powering down unused cores, for instance, or adapting cache configuration to memory usage patterns. This paper proposes a novel prediction strategy that uses nonlinear time-series methods to forecast processor load and cache performance, and evaluates its performance on a set of simple C programs running on an Intel Core® Duo.

Keywords

Average Mutual Information Performance Trace Nonlinear Time Series Cache Performance Topological Conjugacy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Joshua Garland
    • 1
  • Elizabeth Bradley
    • 1
  1. 1.University of ColoradoBoulderUSA

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