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.
- 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.
This is a preview of subscription content, access via your institution.
Tax calculation will be finalised at checkout
Purchases are for personal use onlyLearn about institutional subscriptions
Unable to display preview. Download preview PDF.
Alexander, Z., Mytkowicz, T., Diwan, A., Bradley, E.: Measurement and dynamical analysis of computer performance data. In: Cohen, P.R., Adams, N.M., Berthold, M.R. (eds.) IDA 2010. LNCS, vol. 6065, pp. 18–29. Springer, Heidelberg (2010)
Armstrong, B., Eigenmann, R.: Performance forecasting: Towards a methodology for characterizing large computational applications. In: Proc. of the Int’l Conf. on Parallel Processing, pp. 518–525 (1998)
Bradley, E.: Analysis of time series. In: Berthold, M., Hand, D. (eds.) Intelligent Data Analysis: An Introduction, vol. 2, pp. 199–226. Springer, Heidelberg (2000)
Browne, S., Deane, C., Ho, G., Mucci, P.: PAPI: A portable interface to hardware performance counters. In: Proceedings of Department of Defense HPCMP Users Group Conference. Department of Defense (1999)
Fraser, A., Swinney, H.: Independent coordinates for strange attractors from mutual information. Phys. Rev. A 33(2), 1134–1140 (1986)
Hegger, R., Kantz, H., Schreiber, T.: Practical implementation of nonlinear time series methods: The TISEAN package. Chaos 9(2), 413–435 (1999)
Kantz, H., Schreiber, T.: Nonlinear Time Series Analysis, vol. 2. Cambridge University Press, Cambridge (2003)
Kennel, M., Brown, R., Abarbanel, H.: Determining embedding dimension for phase-space reconstruction using a geometrical construction. Phys. Rev. A 45(6), 3403–3411 (1992)
Lorenz, E.: Atmospheric predictability as revealed by naturally occurring analogues. Journal of the Atmospheric Sciences 26(4), 636–646 (1969)
Meiss, J.: Differential Dynamical Systems. Mathematical Modeling and Computation. Society for Industrial and Applied Mathematics, Philadelphia (2007)
Mytkowicz, T.: Supporting experiments in computer systems research. Ph.D. thesis, University of Colorado (November 2010)
Mytkowicz, T., Diwan, A., Bradley, E.: Computer systems are dynamical systems. Chaos 19(3), 033124–033124–14 (2009)
Sauer, T., Yorke, J., Casdagli, M.: Embedology. Journal of Statistical Physics 65, 579–616 (1991)
Takens, F.: Detecting strange attractors in turbulence. In: Rand, D., Young, L.S. (eds.) Dynamical Systems and Turbulence, Warwick 1980. Lecture Notes in Mathematics, vol. 898, pp. 366–381. Springer, Heidelberg (1981)
Editors and Affiliations
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Garland, J., Bradley, E. (2011). Predicting Computer Performance Dynamics. In: Gama, J., Bradley, E., Hollmén, J. (eds) Advances in Intelligent Data Analysis X. IDA 2011. Lecture Notes in Computer Science, vol 7014. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24800-9_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24799-6
Online ISBN: 978-3-642-24800-9