Performance Forecasting: Towards a Methodology for Characterizing Large Computational Applications
ICPP '98 Proceedings of the 1998 International Conference on Parallel Processing
Intelligent data analysis
Nonlinear Time Series Analysis
Nonlinear Time Series Analysis
Differential Dynamical Systems (Monographs on Mathematical Modeling and Computation)
Differential Dynamical Systems (Monographs on Mathematical Modeling and Computation)
Supporting experiments in computer systems research
Supporting experiments in computer systems research
Measurement and dynamical analysis of computer performance data
IDA'10 Proceedings of the 9th international conference on Advances in Intelligent Data Analysis
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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 Core2® Duo.