Characterizing Time-Varying Program Behavior Using Phase Complexity Surfaces

  • Frederik Vandeputte
  • Lieven Eeckhout
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6760)

Abstract

It is well known that a program exhibits time-varying execution behavior, i.e., a program typically goes through a number of phases during its execution exhibiting relatively homogeneous behavior within a phase and distinct behavior across phases. In fact, several recent research studies have been exploiting this time-varying behavior for various purposes such as simulation acceleration, code optimization, hardware adaptation for reducing energy consumption, etc.

This paper proposes phase complexity surfaces to characterize a computer program’s phase behavior across various time scales in an intuitive manner. The phase complexity surfaces incorporate metrics that characterize phase behavior in terms of the number of phases, their predictability, the degree of variability within and across phases, and the phase behavior’s dependence on the time scale granularity. Leveraging phase complexity surfaces, the paper then characterizes the phase behavior of the SPEC CPU benchmarks across multiple platforms (Alpha and IA-32) and across two CPU benchmark suite generations (CPU2000 and CPU2006).

Keywords

Phase Behavior Benchmark Suite Time Granularity Program Phase Spec CPU2000 
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.

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References

  1. 1.
    Eeckhout, L., Sampson, J., Calder, B.: Exploiting program microarchitecture independent characteristics and phase behavior for reduced benchmark suite simulation. In: Proceedings of the 2005 IEEE International Symposium on Workload Characterization (IISWC), pp. 2–12 (2005)Google Scholar
  2. 2.
    Sherwood, T., Perelman, E., Hamerly, G., Calder, B.: Automatically characterizing large scale program behavior. In: Proceedings of the International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), pp. 45–57 (2002)Google Scholar
  3. 3.
    Balasubramonian, R., Albonesi, D., Buyuktosunoglu, A., Dwarkadas, S.: Memory hierarchy reconfiguration for energy and performance in general-purpose processor architectures. In: Proceedings of the 33th Annual International Symposium on Microarchitecture (MICRO), pp. 245–257 (2000)Google Scholar
  4. 4.
    Dhodapkar, A., Smith, J.E.: Dynamic microarchitecture adaptation via co-designed virtual machines. In: International Solid State Circuits Conference (2002)Google Scholar
  5. 5.
    Dhodapkar, A., Smith, J.E.: Managing multi-configuration hardware via dynamic working set analysis. In: Proceedings of the 29th Annual International Symposium on Computer Architecture (ISCA), pp. 233–244 (2002)Google Scholar
  6. 6.
    Sherwood, T., Sair, S., Calder, B.: Phase tracking and prediction. In: Proceedings of the 30th Annual International Symposium on Computer Architecture (ISCA), pp. 336–347 (2003)Google Scholar
  7. 7.
    Georges, A., Buytaert, D., Eeckhout, L., De Bosschere, K.: Method-level phase behavior in Java workloads. In: Proceedings of the 19th Annual ACM SIGPLAN Conference on Object-Oriented Programming, Languages, Applications and Systems (OOPSLA), pp. 270–287 (2004)Google Scholar
  8. 8.
    Nagpurkar, P., Krintz, C., Sherwood, T.: Phase-aware remote profiling. In: Proceedings of the International Conference on Code Generation and Optimization (CGO), pp. 191–202 (2005)Google Scholar
  9. 9.
    Perelman, E., Hamerly, G., Calder, B.: Picking statistically valid and early simulation points. In: Proceedings of the 12th International Conference on Parallel Architectures and Compilation Techniques (PACT), pp. 244–256 (2003)Google Scholar
  10. 10.
    Patil, H., Cohn, R., Charney, M., Kapoor, R., Sun, A., Karunanidhi, A.: Pinpointing representative portions of large Intel Itanium programs with dynamic instrumentation. In: Proceedings of the 37th Annual International Symposium on Microarchitecture (MICRO), pp. 81–93 (2004)Google Scholar
  11. 11.
    Cho, C.B., Li, T.: Complexity-based program phase analysis and classification. In: Proceedings of the 15th International Conference on Parallel Architectures and Compilation Techniques (PACT), pp. 105–113 (2006)Google Scholar
  12. 12.
    Huffmire, T., Sherwood, T.: Wavelet-based phase classification. In: Proceedings of the 15th International Conference on Parallel Architectures and Compilation Techniques (PACT), pp. 95–104 (2006)Google Scholar
  13. 13.
    Shen, X., Zhong, Y., Ding, C.: Locality phase prediction. In: International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), pp. 165–176 (2004)Google Scholar
  14. 14.
    Lau, J., Perelman, E., Hamerly, G., Sherwood, T., Calder, B.: Motivation for variable length intervals and hierarchical phase behavior. In: Proceedings of the International Symposium on Performance Analysis of Systems and Software (ISPASS), pp. 135–146 (2005)Google Scholar
  15. 15.
    Lau, J., Perelman, E., Calder, B.: Selecting software phase markers with code structure analysis. In: Proceedings of the International Conference on Code Generation and Optimization (CGO), pp. 135–146 (2006)Google Scholar
  16. 16.
    Huang, M., Renau, J., Torrellas, J.: Positional adaptation of processors: Application to energy reduction. In: Proceedings of the 30th Annual International Symposium on Computer Architecture (ISCA), pp. 157–168 (2003)Google Scholar
  17. 17.
    Duesterwald, E., Cascaval, C., Dwarkadas, S.: Characterizing and predicting program behavior and its variability. In: Proceedings of the International Conference on Parallel Architectures and Compilation Techniques (PACT), pp. 220–231 (2003)Google Scholar
  18. 18.
    Isci, C., Martonosi, M.: Identifying program power phase behavior using power vectors. In: Proceedings of the Sixth Annual IEEE International Workshop on Workload Characterization, WWC (2003)Google Scholar
  19. 19.
    Lau, J., Sampson, J., Perelman, E., Hamerly, G., Calder, B.: The strong correlation between code signatures and performance. In: Proceedings of the International Symposium on Performance Analysis of Systems and Software (ISPASS), pp. 236–247 (2005)Google Scholar
  20. 20.
    Lau, J., Schoenmackers, S., Calder, B.: Structures for phase classification. In: Proceedings of the 2004 International Symposium on Performance Analysis of Systems and Software (ISPASS), pp. 57–67 (2004)Google Scholar
  21. 21.
    Cho, C.B., Li, T.: Using wavelet domain workload execution characteristics to improve accuracy, scalability and robustness in program phase analysis. In: Proceedings of the International Symposium on Performance Analysis of Systems and Software, ISPASS (2007)Google Scholar
  22. 22.
    Lau, J., Schoenmackers, S., Calder, B.: Transition phase classification and prediction. In: Proceedings of the 11th International Symposium on High Performance Computer Architecture (HPCA), pp. 278–289 (2005)Google Scholar
  23. 23.
    Vandeputte, F., Eeckhout, L., De Bosschere, K.: A detailed study on phase predictors. In: Proceedings of the 11th International Euro-Par Conference, pp. 571–581 (2005)Google Scholar
  24. 24.
    Sherwood, T., Perelman, E., Calder, B.: Basic block distribution analysis to find periodic behavior and simulation points in applications. In: Proceedings of the International Conference on Parallel Architectures and Compilation Techniques (PACT), pp. 3–14 (2001)Google Scholar
  25. 25.
    Chen, I.K., Coffey, J.T., Mudge, T.N.: Analysis of branch prediction via data compression. In: Proceedings of the 7th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), pp. 128–137 (1996)Google Scholar
  26. 26.
    Burger, D.C., Austin, T.M.: The SimpleScalar Tool Set. Computer Architecture News (1997), http://www.simplescalar.com
  27. 27.
    Maebe, J., Ronsse, M., De Bosschere, K.: DIOTA: Dynamic instrumentation, optimization and transformation of applications. In: Compendium of Workshops and Tutorials Held in Conjunction with PACT 2002: International Conference on Parallel Architectures and Compilation Techniques (2002)Google Scholar
  28. 28.
    Eeckhout, L., Vandierendonck, H., De Bosschere, K.: Workload design: Selecting representative program-input pairs. In: Proceedings of the International Conference on Parallel Architectures and Compilation Techniques (PACT), pp. 83–94 (2002)Google Scholar
  29. 29.
    Johnson, R.A., Wichern, D.W.: Applied Multivariate Statistical Analysis, 5th edn. Prentice Hall, Englewood Cliffs (2002)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Frederik Vandeputte
    • 1
  • Lieven Eeckhout
    • 1
  1. 1.ELIS DepartmentGhent UniversityGentBelgium

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