Switchable Scheduling for Runtime Adaptation of Optimization

  • Lénaïc Bagnères
  • Cédric Bastoul
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8632)

Abstract

Parallel applications used to be executed alone until their termination on partitions of supercomputers: a very static environment for very static applications. The recent shift to multicore architectures for desktop and embedded systems as well as the emergence of cloud computing is raising the problem of the impact of the execution context on performance. The number of criteria to take into account for that purpose is significant: architecture, system, workload, dynamic parameters, etc. Finding the best optimization for every context at compile time is clearly out of reach. Dynamic optimization is the natural solution, but it is often costly in execution time and may offset the optimization it is enabling. In this paper, we present a static-dynamic compiler optimization technique that generates loop-based programs with dynamic auto-tuning capabilities with very low overhead. Our strategy introduces switchable scheduling, a family of program transformations that allows to switch between optimized versions while always processing useful computation. We present both the technique to generate self-adaptive programs based on switchable scheduling and experimental evidence of their ability to sustain high-performance in a dynamic environment.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bastoul, C.: Code generation in the polyhedral model is easier than you think. In: PACT 2013 IEEE International Conference on Parallel Architecture and Compilation Techniques, Juan-les-Pins, France, pp. 7–16 (September 2004)Google Scholar
  2. 2.
    Bodin, F., Kisuki, T., Knijnenburg, P.M.W., O’Boyle, M.F.P., Rohou, E.: Iterative compilation in a non-linear optimisation space. In: W. on Profile and Feedback Directed Compilation, Paris (October 1998)Google Scholar
  3. 3.
    Bondhugula, U., Hartono, A., Ramanujam, J., Sadayappan, P.: A practical automatic polyhedral parallelizer and locality optimizer. In: PLDI 2008 ACM Conf. on Programming language Design and Implementation, Tucson, USA (June 2008)Google Scholar
  4. 4.
    Byler, M., Davies, J.R.B., Huson, C., Leasure, B., Wolfe, M.: Multiple version loops. In: International Conference on Parallel Processing (August 1987)Google Scholar
  5. 5.
    Emani, M., Wang, Z., O’Boyle, M.: Smart, adaptive mapping of parallelism in the presence of external workload. In: 2013 IEEE/ACM International Symposium on Code Generation and Optimization (CGO), pp. 1–10 (2013)Google Scholar
  6. 6.
    Feautrier, P.: Parametric integer programming. RAIRO Recherche Opérationnelle 22(3), 243–268 (1988)MathSciNetMATHGoogle Scholar
  7. 7.
    Feautrier, P.: Some efficient solutions to the affine scheduling problem, part II: multidimensional time. Int. J. of Parallel Programming 21(6), 389–420 (1992)MathSciNetCrossRefMATHGoogle Scholar
  8. 8.
    Girbal, S., Vasilache, N., Bastoul, C., Cohen, A., Parello, D., Sigler, M., Temam, O.: Semi-automatic composition of loop transformations for deep parallelism and memory hierarchies. Int. J. of Parallel Programming 34(3), 261–317 (2006)CrossRefMATHGoogle Scholar
  9. 9.
    Jimborean, A., Mastrangelo, L., Loechner, V., Clauss, P.: VMAD: An Advanced Dynamic Program Analysis & Instrumentation Framework. In: O’Boyle, M. (ed.) CC 2012. LNCS, vol. 7210, pp. 220–239. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  10. 10.
    Luk, C.-K., Hong, S., Kim, H.: Qilin: Exploiting parallelism on heterogeneous multiprocessors with adaptive mapping. In: MICRO-42. 42nd Annual IEEE/ACM International Symposium on Microarchitecture, pp. 45–55 (December 2009)Google Scholar
  11. 11.
    Pouchet, L.-N., Bastoul, C., Cohen, A., Cavazos, J.: Iterative optimization in the polyhedral model: Part II, multidimensional time. In: ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI 2008), Tucson, Arizona, pp. 90–100. ACM Press (June 2008)Google Scholar
  12. 12.
    Pouchet, L.-N., Bondhugula, U., Bastoul, C., Cohen, A., Ramanujam, J., Sadayappan, P.: Combined iterative and model-driven optimization in an automatic parallelization framework. In: SC 2010, New Orleans, USA (November 2010)Google Scholar
  13. 13.
    Pradelle, B., Clauss, P., Loechner, V.: Adaptive Runtime Selection of Parallel Schedules in the Polytope Model. In: 19th High Performance Computing Symposium - HPC 2011. United States, Boston (2011)Google Scholar
  14. 14.
    Rauchwerger, L., Padua, D.: The LRPD test: speculative run-time parallelization of loops with privatization and reduction parallelization. In: Proceedings of the ACM SIGPLAN 1995 Conference on Programming Language Design and Implementation, PLDI 1995, pp. 218–232. ACM, New York (1995)CrossRefGoogle Scholar
  15. 15.
    Steffan, J.G., Colohan, C., Zhai, A., Mowry, T.C.: The stampede approach to thread-level speculation. ACM Trans. Comput. Syst. 23(3), 253–300 (2005)CrossRefGoogle Scholar
  16. 16.
    Tavarageri, S., Pouchet, L.-N., Ramanujam, J., Rountev, A., Sadayappan, P.: Dynamic selection of tile sizes. In: 18th IEEE Int. Conf. on High Performance Computing (HiPC 2011), Bangalore, India (December 2011)Google Scholar
  17. 17.
    Upadrasta, R., Cohen, A.: Sub-polyhedral scheduling using (unit-)two-variable-per-inequality polyhedra. In: ACM Symposium on Principles of Programming Languages, POPL 2013, Rome, Italy, pp. 483–496 (2013)Google Scholar
  18. 18.
    Voss, M., Eigenmann, R.: ADAPT: Automated de-coupled adaptive program transformation. In: Int. Conf. on Parallel Processing, pp. 163–170 (2000)Google Scholar
  19. 19.
    Whaley, C., Petitet, A., Dongarra, J.J.: Automated empirical optimization of software and the ATLAS project. Parallel Computing 27(1–2), 3–35 (2000)Google Scholar
  20. 20.
    Wolfe, M.: High performance compilers for parallel computing. Addison-Wesley Publishing Company (1995)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Lénaïc Bagnères
    • 1
  • Cédric Bastoul
    • 2
  1. 1.University of Paris-Sud and InriaOrsayFrance
  2. 2.University of Strasbourg and InriaStrasbourgFrance

Personalised recommendations