Off-Road Performance Modeling – How to Deal with Segmented Data

  • M. Kashif IlyasEmail author
  • Alexandru Calotoiu
  • Felix Wolf
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10417)


Besides correctness, scalability is one of the top priorities of parallel programmers. With manual analytical performance modeling often being too laborious, developers increasingly resort to empirical performance modeling as a viable alternative, which learns performance models from a limited amount of performance measurements. Although powerful automatic techniques exist for this purpose, they usually struggle with the situation where performance data representing two or more different phenomena are conflated into a single performance model. This not only generates an inaccurate model for the given data, but can also either fail to point out existing scalability issues or create the appearance of such issues when none are present. In this paper, we present an algorithm to detect segmentation in a sequence of performance measurements and estimate the point where the behavior changes. Our method correctly identified segmentation in more than 80% of 5.2 million synthetic tests and confirmed expected segmentation in three application case studies.


Parallel computing Performance tools Performance modeling 



This work was supported in part by the German Research Foundation (DFG) through the Priority Programme 1648 Software for Exascale Computing (SPPEXA) and the Programme Performance Engineering for Scientific Software. Additional support was provided by the German Federal Ministry of Education and Research (BMBF) under Grant No. 01IH16008, and by the US Department of Energy under Grant No. DE-SC0015524. Finally, we would like to thank the University Computing Center (Hochschulrechenzentrum) of TU Darmstadt for providing us with access to the Lichtenberg Cluster.


  1. 1.
    Auger, I., Lawrance, C.: Algorithms for the optimal identification of segment neighborhoods. Bull. Math. Biol. 51(1), 39–54 (1989)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Calotoiu, A., Hoefler, T., Poke, M., Wolf, F.: Using automated performance modeling to find scalability bugs in complex codes. In: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, SC 2013, November 2013Google Scholar
  3. 3.
    Chang, S., Lu, K., Yang, M.: Fuzzy change-point algorithms for regression models. IEEE Trans. Fuzzy Syst. 23, 2343–2357 (2015)CrossRefGoogle Scholar
  4. 4.
    Dennis, J.M., Edwards, J., Evans, K.J., Guba, O., Lauritzen, P.H., Mirin, A.A., St-Cyr, A., Taylor, M.A., Worley, P.H.: CAM-SE: a scalable spectral element dynamical core for the community atmosphere model. Int. J. High Perform. Comput. Appl. 26, 74–89 (2012)CrossRefGoogle Scholar
  5. 5.
    Fagg, G.E., Pjesivac-grbovic, J., Bosilca, G., Dongarra, J.J., Jeannot, E.: Flexible collective communication tuning architecture applied to OpenMPI. In: 2006 Euro PVM/MPI (2006)Google Scholar
  6. 6.
    Jackson, B., Sargle, J.D., Barnes, D., Arabhi, S., Alt, A., Gioumousis, P., Gwin, E., Sangtrakulcharoen, P., Tan, L., Tsai, T.T.: An algorithm for optimal partitioning of data on an interval. Sig. Process. Lett. 12(2), 105–108 (2005)CrossRefGoogle Scholar
  7. 7.
    Killick, R., Fearnhead, P., Eckley, I.: Optimal detection of change points with a linear computational cost. J. Am. Stat. Assoc. 107, 1590–1598 (2012)CrossRefzbMATHGoogle Scholar
  8. 8.
    Scott, A., Knott, M.: A cluster analysis method for grouping means in the analysis of variance. Biometrics 30, 507–512 (1974)CrossRefzbMATHGoogle Scholar
  9. 9.
    Steve, H.: \(Intel^{\textregistered }\) MPI library collective optimization on the Intel Xeon Phi coprocessor using environment variable collective operation control (2015).
  10. 10.
    Thakur, R., Gropp, W.D.: Improving the performance of collective operations in MPICH. In: Dongarra, J., Laforenza, D., Orlando, S. (eds.) EuroPVM/MPI 2003. LNCS, vol. 2840, pp. 257–267. Springer, Heidelberg (2003). doi: 10.1007/978-3-540-39924-7_38 CrossRefGoogle Scholar
  11. 11.
    Zhang, B., Geng, J., Lai, L.: Change-point estimation in high dimensional linear regression models via sparse group LASSO. In: 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton), pp. 815–821 (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • M. Kashif Ilyas
    • 1
    Email author
  • Alexandru Calotoiu
    • 1
  • Felix Wolf
    • 1
  1. 1.Technische Universität DarmstadtDarmstadtGermany

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