Abstract
A mixed method combining formal and auto-tuning approaches and aimed at maximizing efficiency of parallel programs (in terms of execution time) is proposed. The formal approach is based on algorithmic algebra and the usage of tools for automated design and synthesis of programs based on high-level algorithm specifications (schemes). Parallel software auto-tuning is the method of adjusting some structural parameters of a program to a target hardware platform to speed-up computation as much as possible. Previously, we have developed a framework intended to automate the generation of an auto-tuner from a program source code. However, auto-tuning for complex and nontrivial parallel systems is usually time-consuming due to empirical evaluation of huge amount of parameter values combinations of an initial parallel program in a target environment. In this paper, we extend our approach with statistical modeling and neural network algorithms that allow to reduce significantly the space of possible parameter combinations. The improvement consists in automatic training of a neural network model on results of “traditional” tuning cycles and the subsequent replacement of some auto-tuner calls with an evaluation from the statistical model. The method allows, particularly, transferring knowledge about the influence of parameters on program performance between “similar” (in terms of hardware architecture) computing environments for the same applications. The idea is to reuse a model trained on data from a similar environment. The use of the method is illustrated by an example of tuning a parallel sorting program which combines several sorting methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Naono, K., Teranishi, K., Cavazos, J., Suda, R.: Software Automatic Tuning: From Concepts to State-of-the-Art Results. Springer, Berlin (2010). https://doi.org/10.1007/978-1-4419-6935-4
Durillo, J., Fahringer, T.: From single- to multi-objective auto-tuning of programs: advantages and implications. Sci. Program. 22(4), 285–297 (2014)
Doroshenko, A., Shevchenko, R.: A rewriting framework for rule-based programming dynamic applications. Fundamenta Informaticae 72(1–3), 95–108 (2006)
Andon, P.I., Doroshenko, A.Y., Tseytlin, G.O., Yatsenko, O.A.: Algebra-Algorithmic Models and Methods of Parallel Programming. Akademperiodyka, Kyiv (2007). (in Russian)
Yatsenko, O.: On parameter-driven generation of algorithm schemes. In: Popova-Zeugmann, L. (ed.) CS&P’2012, pp. 428–438. Humboldt University Press, Berlin (2012)
Doroshenko, A., Zhereb, K., Yatsenko, O.: Developing and optimizing parallel programs with algebra-algorithmic and term rewriting tools. In: Ermolayev, V., Mayr, H.C., Nikitchenko, M., Spivakovsky, A., Zholtkevych, G. (eds.) ICTERI 2013. CCIS, vol. 412, pp. 70–92. Springer, Cham (2013). https://doi.org/10.1007/978-3-319-03998-5_5
Ivanenko, P., Doroshenko, A., Zhereb, K.: TuningGenie: auto-tuning framework based on rewriting rules. In: Ermolayev, V., Mayr, H., Nikitchenko, M., Spivakovsky, A., Zholtkevych, G. (eds.) ICTERI 2014. CCIS, vol. 469, pp. 139–158. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-13206-8_7
Doroshenko, A., Ivanenko, P., Ovdii, O., Yatsenko, O.: Automated program design—an example solving a weather forecasting problem. Open Phys. 14(1), 410–419 (2016)
Doroshenko, A., Ivanenko, P., Novak, O., Yatsenko, O.: Optimization of parallel software tuning with statistical modeling and machine learning. In: Ermolayev, V., et al. (eds.) ICTERI 2018. Communications in Computer and Information Science, vol. 2105, pp. 219–226. Springer, Cham (2018)
TermWare Tutorial. http://www.gradsoft.ua/rus/Products/termware/docs/tutorial_eng.html. Accessed 30 Nov 2018
Mitchell, T.M.: Machine Learning, 1st edn. McGraw-Hill Education, New York (1997)
Givens, G.H., Hoeting, J.A.: Computational Statistics, 2nd edn. Wiley, Chichester (2012)
Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques, 3rd edn. Morgan Kaufmann, Burlington (2011)
Fawcett, T.: An introduction to ROC analysis. Pattern Recogn. Lett. 27(8), 861–874 (2006)
Class RecursiveAction (Java SE 9 & JDK 9) – Oracle Help Center. https://docs.oracle.com/javase/9/docs/api/java/util/concurrent/RecursiveAction.html. Accessed 30 Nov 2018
Class ForkJoinTask (Java SE 9 & JDK 9) – Oracle Help Center. https://docs.oracle.com/javase/9/docs/api/java/util/concurrent/ForkJoinTask.html. Accessed 30 Nov 2018
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Crawley, M.J.: The R Book, 1st edn. Wiley, Chichester (2012)
Fletcher, R.: Practical Methods of Optimization, 2nd edn. Wiley, Chichester (2000)
Karcher, T., Schaefer, C., Pankratius, V.: Auto-tuning support for manycore applications: perspectives for operating systems and compilers. ACM SIGOPS Oper. Syst. Rev. 43(2), 96–97 (2009)
Whaley, R., Petitet, A., Dongarra, J.J.: Automated empirical optimizations of software and the ATLAS Project. Parallel Comput. 27(1–2), 3–35 (2001)
Frigo, M., Johnson, S.: FFTW: an adaptive software architecture for the FF. Acoust. Speech Sig. Process. 3, 1381–1384 (1998)
Schaefer, C.A., Pankratius, V., Tichy, W.F.: Atune-IL: an instrumentation language for auto-tuning parallel applications. In: Sips, H., Epema, D., Lin, H.-X. (eds.) Euro-Par 2009. LNCS, vol. 5704, pp. 9–20. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-03869-3_5
Tapus, C., Chung, I.-H., Hollingsworth, J.K.: Active harmony: towards automated performance tuning. In: 2002 ACM/IEEE Conference on Supercomputing, SC 2002, pp. 1–11. IEEE Computer Society, Los Alamitos (2002)
Yi, Q., Seymour, K., You, H., Vuduc, R., Quinla, D.: POET: parameterized optimizations for empirical tuning. In: Parallel and Distributed Processing Symposium 2007, IPDPS 2007, p. 447. IEEE Computer Society, Piscataway (2007)
Katagiri, T., Kise, K., Honda, H., Yuba, T.: FIBER: a generalized framework for auto-tuning software. In: Veidenbaum, A., Joe, K., Amano, H., Aiso, H. (eds.) ISHPC 2003. LNCS, vol. 2858, pp. 146–159. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-39707-6_11
Pekhimenko, G., Brown, A.D.: Efficient program compilation through machine learning techniques. In: Naono, K., Teranishi, K., Cavazos, J., Suda, R. (eds.) Software Automatic Tuning, pp. 335–351. Springer, New York (2010). https://doi.org/10.1007/978-1-4419-6935-4_19
Fursin, G., et al.: Milepost GCC: machine learning enabled self-tuning compiler. Int. J. Parallel Program. 39(3), 296–327 (2011)
Plotnikov, D., Melnik, D., Vardanyan, M., Buchatskiy, R., Zhuykov, R., Lee, J.-H.: Automatic tuning of compiler optimizations and analysis of their impact. In: 8th International Workshop on Automatic Performance Tuning (iWAPT 2013). Procedia Computer Science, vol. 18, pp. 1312–1321. Elsevier B.V., Amsterdam (2013)
Hutter, F., Hoos, H.H., Leyton-Brown, K.: Sequential model-based optimization for general algorithm configuration. In: Coello, C.A.C. (ed.) LION 2011. LNCS, vol. 6683, pp. 507–523. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25566-3_40
Eggensperger, K., Hutter, F., Hoos, H.H., Leyton-Brown, K.: Efficient benchmarking of hyperparameter optimizers via surrogates. In: 29th AAAI Conference on Artificial Intelligence (AAAI 2015), pp. 1114–1120. AAAI Press, Palo Alto (2015)
Rahman, M., Pouchet, L.-N., Sadayappan, P.: Neural network assisted tile size selection. In: 5th International Workshop on Automatic Performance Tuning (iWAPT 2010), pp. 1–15. Springer, Berkeley (2010)
Kofler, K., Grasso, I., Cosenza, B., Fahringer, T.: An automatic input-sensitive approach for heterogeneous task partitioning. In: 27th ACM International Conference on Supercomputing (ICS 2013), pp. 149–160. ACM, New York (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Doroshenko, A., Ivanenko, P., Novak, O., Yatsenko, O. (2019). A Mixed Method of Parallel Software Auto-Tuning Using Statistical Modeling and Machine Learning. In: Ermolayev, V., Suárez-Figueroa, M., Yakovyna, V., Mayr, H., Nikitchenko, M., Spivakovsky, A. (eds) Information and Communication Technologies in Education, Research, and Industrial Applications. ICTERI 2018. Communications in Computer and Information Science, vol 1007. Springer, Cham. https://doi.org/10.1007/978-3-030-13929-2_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-13929-2_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-13928-5
Online ISBN: 978-3-030-13929-2
eBook Packages: Computer ScienceComputer Science (R0)