Skip to main content

Advertisement

SpringerLink
Log in
Menu
Find a journal Publish with us
Search
Cart
Book cover

European Conference on Parallel Processing

Euro-Par 2012: Euro-Par 2012 Parallel Processing pp 89–101Cite as

  1. Home
  2. Euro-Par 2012 Parallel Processing
  3. Conference paper
ASK: Adaptive Sampling Kit for Performance Characterization

ASK: Adaptive Sampling Kit for Performance Characterization

  • Pablo de Oliveira Castro19,
  • Eric Petit20,
  • Jean Christophe Beyler21 &
  • …
  • William Jalby19 
  • Conference paper
  • 2953 Accesses

  • 4 Citations

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 7484)

Abstract

Characterizing performance is essential to optimize programs and architectures. The open source Adaptive Sampling Kit (ASK) measures the performance trade-offs in large design spaces. Exhaustively sampling all points is computationally intractable. Therefore, ASK concentrates exploration in the most irregular regions of the design space through multiple adaptive sampling methods. The paper presents the ASK architecture and a set of adaptive sampling strategies, including a new approach: Hierarchical Variance Sampling. ASK’s usage is demonstrated on two performance characterization problems: memory stride accesses and stencil codes. ASK builds precise models of performance with a small number of measures. It considerably reduces the cost of performance exploration. For instance, the stencil code design space, which has more than 31.108 points, is accurately predicted using only 1 500 points.

Keywords

  • Root Mean Square Error
  • Design Space
  • Adaptive Sampling
  • Performance Characterization
  • Irregular Region

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.

Download conference paper PDF

References

  1. Simpson, T., Lin, D., Chen, W.: Sampling strategies for computer experiments: design and analysis. International Journal of Reliability and Applications 2(3), 209–240 (2001)

    Google Scholar 

  2. Stein, M.: Large sample properties of simulations using Latin hypercube sampling. Technometrics, 143–151 (1987)

    Google Scholar 

  3. Johnson, M., Moore, L., Ylvisaker, D.: Minimax and maximin distance designs. Journal of Statistical Planning and Inference 26(2), 131–148 (1990)

    CrossRef  MathSciNet  Google Scholar 

  4. Diwekar, U., Kalagnanam, J.: Efficient sampling technique for optimization under uncertainty. AIChE Journal 43(2), 440–447 (1997)

    CrossRef  Google Scholar 

  5. Li, B., Peng, L., Ramadass, B.: Accurate and efficient processor performance prediction via regression tree based modeling. Journal of Systems Architecture 55(10-12), 457–467 (2009)

    CrossRef  Google Scholar 

  6. Ridgeway, G.: Generalized Boosted Models: A guide to the gbm package. Update 1, 1 (2007)

    Google Scholar 

  7. Gramacy, R., Lee, H.: Adaptive design and analysis of supercomputer experiments. Technometrics 51(2), 130–145 (2009)

    CrossRef  MathSciNet  Google Scholar 

  8. Gramacy, R.: tgp: An R package for Bayesian nonstationary, semiparametric nonlinear regression and design by treed gaussian process models. Journal of Statistical Software 19(9), 6 (2007)

    Google Scholar 

  9. Cohn, D.: Neural network exploration using optimal experiment design. Neural Networks 9(6), 1071–1083 (1996)

    CrossRef  Google Scholar 

  10. Settles, B.: Active Learning Literature Survey. Science 10(3), 237–304 (1995)

    Google Scholar 

  11. Gorissen, D., Couckuyt, I., Demeester, P., Dhaene, T., Crombecq, K.: A surrogate modeling and adaptive sampling toolbox for computer based design. The Journal of Machine Learning Research 11, 2051–2055 (2010)

    Google Scholar 

  12. Crombecq, K., Gorissen, D., Deschrijver, D., Dhaene, T.: A Novel Hybrid Sequential Design Strategy for Global Surrogate Modeling of Computer Experiments. SIAM Journal on Scientific Computing 33, 1948 (2011)

    CrossRef  MathSciNet  MATH  Google Scholar 

  13. Dasgupta, S., Hsu, D.: Hierarchical sampling for active learning. In: Proceedings of the 25th International Conference on Machine learning, pp. 208–215. ACM (2008)

    Google Scholar 

  14. Breiman, L., Friedman, J., Olshen, R., Stone, C., Steinberg, D., Colla, P.: CART: Classification and regression trees. Wadsworth, Belmont (1983)

    Google Scholar 

  15. Atkinson, E., Therneau, T.: An introduction to recursive partitioning using the RPART routines. Mayo Foundation, Rochester (2000)

    Google Scholar 

  16. McKay, A.: Distribution of the Coefficient of Variation and the Extended t Distribution. Journal of the Royal Statistical Society 95(4), 695–698 (1932)

    CrossRef  Google Scholar 

  17. Friedman, J.: Greedy function approximation: a gradient boosting machine. Annals of Statistics, 1189–1232 (2001)

    Google Scholar 

  18. Datta, K., Murphy, M., Volkov, V., Williams, S., Carter, J., Oliker, L., Patterson, D., Shalf, J., Yelick, K.: Stencil computation optimization and auto-tuning on state-of-the-art multicore architectures. In: Proceedings of the 2008 ACM/IEEE Conference on Supercomputing, pp. 1–12. IEEE Press (2008)

    Google Scholar 

  19. Treibig, J., Wellein, G., Hager, G.: Efficient multicore-aware parallelization strategies for iterative stencil computations. J. Comput. Science 2(2), 130–137 (2011)

    CrossRef  Google Scholar 

  20. Dursun, H., Nomura, K.-i., Peng, L., Seymour, R., Wang, W., Kalia, R.K., Nakano, A., Vashishta, P.: A Multilevel Parallelization Framework for High-Order Stencil Computations. In: Sips, H., Epema, D., Lin, H.-X. (eds.) Euro-Par 2009. LNCS, vol. 5704, pp. 642–653. Springer, Heidelberg (2009)

    CrossRef  Google Scholar 

  21. Maron, O., Moore, A.: Hoeffding races: Accelerating model selection search for classification and function approximation. Robotics Institute, 263 (1993)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Exascale Computing Research, University of Versailles - UVSQ, France

    Pablo de Oliveira Castro & William Jalby

  2. LRC ITACA, University of Versailles - UVSQ, France

    Eric Petit

  3. Intel Corporation, USA

    Jean Christophe Beyler

Authors
  1. Pablo de Oliveira Castro
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eric Petit
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jean Christophe Beyler
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. William Jalby
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. University of Patras, Computer Technology Institute and Press “Diophantus”,, N. Kazantzaki, 26504, Rio, Greece

    Christos Kaklamanis

  2. University of Patras, University Building B, 26504, Rio, Greece

    Theodore Papatheodorou

  3. Computer Technology Institute and Press “Diophantus”, University of Patras, N. Kazantzaki, 26504, Rio, Greece

    Paul G. Spirakis

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

de Oliveira Castro, P., Petit, E., Beyler, J.C., Jalby, W. (2012). ASK: Adaptive Sampling Kit for Performance Characterization. In: Kaklamanis, C., Papatheodorou, T., Spirakis, P.G. (eds) Euro-Par 2012 Parallel Processing. Euro-Par 2012. Lecture Notes in Computer Science, vol 7484. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32820-6_11

Download citation

  • .RIS
  • .ENW
  • .BIB
  • DOI: https://doi.org/10.1007/978-3-642-32820-6_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32819-0

  • Online ISBN: 978-3-642-32820-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Share this paper

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Search

Navigation

  • Find a journal
  • Publish with us

Discover content

  • Journals A-Z
  • Books A-Z

Publish with us

  • Publish your research
  • Open access publishing

Products and services

  • Our products
  • Librarians
  • Societies
  • Partners and advertisers

Our imprints

  • Springer
  • Nature Portfolio
  • BMC
  • Palgrave Macmillan
  • Apress
  • Your US state privacy rights
  • Accessibility statement
  • Terms and conditions
  • Privacy policy
  • Help and support

167.114.118.210

Not affiliated

Springer Nature

© 2023 Springer Nature