Abstract
Computers are nonlinear dynamical systems that exhibit complex and sometimes even chaotic behavior. The low-level performance models used in the computer systems community, however, are linear. This paper is an exploration of that disconnect: when linear models are adequate for predicting computer performance and when they are not. Specifically, we build linear and nonlinear models of the processor load of an Intel i7-based computer as it executes a range of different programs. We then use those models to predict the processor loads forward in time and compare those forecasts to the true continuations of the time series.
Keywords
- Multiple Linear Regression
- Multiple Linear Regression Model
- Computer Performance
- Iterate Function System
- Prediction Horizon
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.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Alexander, Z., Bradley, E., Garland, J., Meiss, J.: Iterated function system models in data analysis: Detection and separation. CHAOS 22(2) (April 2012)
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)
Bass, T.: The Eudaemonic Pie. Penguin, New York (1992)
Browne, S., Deane, C., Ho, G., Mucci, P.: PAPI: A portable interface to hardware performance counters. In: Proceedings of Department of the Defense HPCMP Users Group Conference (1999)
Casdagli, M., Eubank, S. (eds.): Nonlinear Modeling and Forecasting. Addison Wesley (1992)
Cavazos, J., Fursin, G., Agakov, F., Bonilla, E., O’Boyle, M.F.P., Temam, O.: Rapidly selecting good compiler optimizations using performance counters. In: Proceedings of the International Symposium on Code Generation and Optimization (2007)
Desikan, R., Burger, D., Keckler, S.W.: Measuring experimental error in microprocessor simulation. In: Proceedings of the International Symposium on Computer Architecture (2001)
Faraway, J.J.: Practical regression and ANOVA in R (2002), http://cran.r-project.org/doc/contrib/Faraway-PRA.pdf
Fraser, A., Swinney, H.: Independent coordinates for strange attractors from mutual information. Physical Review A 33(2), 1134–1140 (1986)
Garland, J., Bradley, E.: Predicting computer performance dynamics. In: Gama, J., Bradley, E., Hollmén, J. (eds.) IDA 2011. LNCS, vol. 7014, pp. 173–184. Springer, Heidelberg (2011)
Grassberger, P., Hegger, R., Kantz, H., Schaffrath, C., Schreiber, T.: On noise reduction methods for chaotic data. Chaos 3, 127 (1993)
Grechanik, M., Fu, C., Xie, Q.: Automatically finding performance problems with feedback-directed learning software testing. In: Proceedings of the International Conference on Software Engineering (2012)
Jain, R.: The Art of Computer Systems Performance Analysis: Techniques for Experimental Design, Measurement, Simulation, and Modeling, 2nd edn. John Wiley & Sons (1991)
Kantz, H., Schreiber, T.: Nonlinear Time Series Analysis. Cambridge University Press (1997)
Kennel, M., Isabelle, S.: Method to distinguish possible chaos from colored noise and to determine embedding parameters. Phys. Rev. A 46, 3111 (1992)
Kennel, M.B., Brown, R., Abarbanel, H.D.I.: Determining minimum embedding dimension using a geometrical construction. Physical Review A 45, 3403–3411 (1992)
Liu, J., Wu, S., Zidek, J.: On segmented multivariate regression. Statistica Sinica 7, 497–525 (1997)
Lorenz, E.N.: Atmospheric predictability as revealed by naturally occurring analogues. Journal of the Atmospheric Sciences 26, 636–646 (1969)
Makhoul, J.: Linear prediction: A tutorial review. Proceedings of the IEEE 63(4), 561–580 (1975)
Martinez, J.F., Ipek, E.: Dynamic multicore resource management: A machine learning approach. IEEE Micro 29(5), 8–17 (2009)
McGee, V.E., Carleton, W.T.: Piecewise regression. Journal of the American Statistical Association 65(331), 1109–1124 (1970)
Moseley, T., Kihm, J.L., Connors, D.A., Grunwald, D.: Methods for modeling resource contention on simultaneous multithreading processors. In: Proceedings of the International Conference on Computer Design (2005)
Myktowicz, T., Diwan, A., Bradley, E.: Computers are dynamical systems. Chaos 19, 033124 (2009), doi:10.1063/1.3187791
Mytkowicz, T.: Supporting experiments in computer systems research. Ph.D. thesis, University of Colorado (November 2010)
Packard, N., Crutchfield, J., Farmer, J., Shaw, R.: Geometry from a time series. Physical Review Letters 45, 712 (1980)
Sauer, T., Yorke, J., Casdagli, M.: Embedology. Journal of Statistical Physics 65, 579–616 (1991)
Takens, F.: Detecting strange attractors in fluid turbulence. In: Rand, D., Young, L.S. (eds.) Dynamical Systems and Turbulence, Springer, Berlin (1981)
Weigend, A., Gershenfeld, N. (eds.): Time Series Prediction: Forecasting the Future and Understanding the Past. Santa Fe Institute (1993)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Garland, J., Bradley, E. (2013). On the Importance of Nonlinear Modeling in Computer Performance Prediction. In: Tucker, A., Höppner, F., Siebes, A., Swift, S. (eds) Advances in Intelligent Data Analysis XII. IDA 2013. Lecture Notes in Computer Science, vol 8207. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41398-8_19
Download citation
DOI: https://doi.org/10.1007/978-3-642-41398-8_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41397-1
Online ISBN: 978-3-642-41398-8
eBook Packages: Computer ScienceComputer Science (R0)