Abstract
Automatic algorithm configuration techniques have proved to be successful in finding performance-optimizing parameter settings of many search-based decision and optimization algorithms. A recurrent, important step in software development is the compilation of source code written in some programming language into machine-executable code. The generation of performance-optimized machine code itself is a difficult task that can be parametrized in many different possible ways. While modern compilers usually offer different levels of optimization as possible defaults, they have a larger number of other flags and numerical parameters that impact properties of the generated machine-code. While the generation of performance-optimized machine code has received large attention and is dealt with in the research area of auto-tuning, the usage of standard automatic algorithm configuration software has not been explored, even though, as we show in this article, the performance of the compiled code has significant stochasticity, just as standard optimization algorithms. As a practical case study, we consider the configuration of the well-known GNU compiler collection (GCC) for minimizing the run-time of machine code for various heuristic search methods. Our experimental results show that, depending on the specific code to be optimized, improvements of up to 40% of execution time when compared to the -O2 and -O3 optimization flags is possible.
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
Notes
- 1.
The GCC optimization options are available at https://gcc.gnu.org/onlinedocs/gcc-7.1.0/gcc/Optimize-Options.html and the parameter definition can be obtained in the params.def file in the source code of GCC.
References
Ansel, J., Kamil, S., Veeramachaneni, K., Ragan-Kelley, J., Bosboom, J., O’Reilly, U.M., Amarasinghe, S.: OpenTuner: an extensible framework for program autotuning. In: Proceedings of the 23rd International Conference on Parallel Architectures and Compilation, pp. 303–315. ACM, New York (2014)
Ansótegui, C., Sellmann, M., Tierney, K.: A gender-based genetic algorithm for the automatic configuration of algorithms. In: Gent, I.P. (ed.) CP 2009. LNCS, vol. 5732, pp. 142–157. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04244-7_14
Birattari, M., Yuan, Z., Balaprakash, P., Stützle, T.: F-race and iterated F-race: an overview. In: Bartz-Beielstein, T., Chiarandini, M., Paquete, L., Preuss, M. (eds.) Experimental Methods for the Analysis of Optimization Algorithms, pp. 311–336. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-02538-9_13
Blackmore, C., Ray, O., Eder, K.: Automatically tuning the GCC compiler to optimize the performance of applications running on the ARM cortex-M3. Technical report, CoRR (2017). https://arxiv.org/abs/1703.08228
Christen, M., Schenk, O., Burkhart, H.: PATUS: a code generation and autotuning framework for parallel iterative stencil computations on modern microarchitectures. In: Proceedings of the 2011 IEEE International Parallel & Distributed Processing Symposium, IPDPS 2011, pp. 676–687. IEEE Computer Society (2011)
Frigo, M., Johnson, S.G.: The design and implementation of FFTW3. Proc. IEEE 93(2), 216–231 (2005). Special Issue on “Program Generation, Optimization, and Platform Adaptation”
Fursin, G., Kashnikov, Y., Memon, A.W., Chamski, Z., Temam, O., Namolaru, M., Yom-Tov, E., Mendelson, B., Zaks, A., Courtois, E., Bodin, F., Barnard, P., Ashton, E., Bonilla, E., Thomson, J., Williams, C.K.I., O’Boyle, M.: Milepost GCC: machine learning enabled self-tuning compiler. Int. J. Parallel Prog. 39(3), 296–327 (2011)
GNU Project, Free Software Foundation: GCC, the GNU compiler collection (1987). https://www.gcc.gnu.org
Helsgaun, K.: An effective implementation of the Lin-Kernighan traveling salesman heuristic. Eur. J. Oper. Res. 126, 106–130 (2000)
Helsgaun, K.: General \(k\)-opt submoves for the Lin-Kernighan TSP heuristic. Math. Program. Comput. 1(2–3), 119–163 (2009)
Henning, J.L.: SPEC CPU2000: measuring CPU performance in the new millennium. Computer 33(7), 28–35 (2000)
Hoos, H.H.: Automated algorithm configuration and parameter tuning. In: Hamadi, Y., Monfroy, E., Saubion, F. (eds.) Autonomous Search, pp. 37–71. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21434-9_3
Hoste, K., Eeckhout, L.: Cole: compiler optimization level exploration. In: Proceedings of the 6th Annual IEEE/ACM International Symposium on Code Generation and Optimization, CGO 2008, pp. 165–174. ACM Press, New York (2008)
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
Hutter, F., Hoos, H.H., Leyton-Brown, K., Stützle, T.: ParamILS: an automatic algorithm configuration framework. J. Artif. Intell. Res. 36, 267–306 (2009)
Ladd, S.R.: ACOVEA (Analysis of compiler options via evolutionary algorithm) (2000). https://github.com/Acovea/libacovea
López-Ibáñez, M., Dubois-Lacoste, J., Pérez Cáceres, L., Stützle, T., Birattari, M.: The irace package: iterated racing for automatic algorithm configuration. Operat. Res. Perspect. 3, 43–58 (2016)
Pérez Cáceres, L., Bischl, B., Stützle, T.: Evaluating random forest models for irace. In: GECCO 2017 Companion. ACM Press (2017)
Pérez Cáceres, L., Pagnozzi, F., Franzin, A., Stützle, T.: Automatic configuration of GCC using irace: supplementary material (2017). http://iridia.ulb.ac.be/supp/IridiaSupp2017-009/
Plotnikov, D., Melnik, D., Vardanyan, M., Buchatskiy, R., Zhuykov, R., Lee, J.H.: Automatic tuning of compiler optimizations and analysis of their impact. In: Alexandrov, V., et al. (eds.) 2013 International Conference on Computational Science. Procedia Computer Science, vol. 18, pp. 1312–1321. Elsevier, Amsterdam (2013)
Püschel, M., Franchetti, F., Voronenko, Y.: Spiral. In: Padua, D. (ed.) Encyclopedia of Parallel Computing, pp. 1920–1933. Springer, Boston (2011). https://doi.org/10.1007/978-0-387-09766-4_244
Siegel, S., Castellan Jr., N.J.: Non Parametric Statistics for the Behavioral Sciences, 2nd edn. McGraw Hill, New York (1988)
Stützle, T.: ACOTSP: a software package of various ant colony optimization algorithms applied to the symmetric traveling salesman problem (2002). http://www.aco-metaheuristic.org/aco-code/
Stützle, T., López-Ibáñez, M.: Automatic (offline) configuration of algorithms. In: Laredo, J.L.J., et al. (eds.) GECCO (Companion), pp. 681–702. ACM Press, New York (2015)
Whaley, C.R.: Atlas (automatically tuned linear algebra software). In: Padua, D. (ed.) Encyclopedia of Parallel Computing, pp. 95–101. Springer, Boston (2011). https://doi.org/10.1007/978-0-387-09766-4
Wright, M.N., Ziegler, A.: ranger: a fast implementation of random forests for high dimensional data in C++ and R. Arxiv preprint arXiv:1508.04409 [stat.ML] (2015)
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: improving the strength pareto evolutionary algorithm for multiobjective optimization. In: Giannakoglou, K.C., et al. (eds.) EUROGEN, pp. 95–100. CIMNE, Barcelona (2002)
Acknowledgments
We acknowledge support from the COMEX project (P7/36) within the IAP Programme of the BelSPO. Thomas Stützle acknowledges support from the Belgian F.R.S.-FNRS, of which he is a senior research associate. The authors would like to thank Manuel López-Ibáñez for his many helpful remarks and assistance.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Pérez Cáceres, L., Pagnozzi, F., Franzin, A., Stützle, T. (2018). Automatic Configuration of GCC Using Irace. In: Lutton, E., Legrand, P., Parrend, P., Monmarché, N., Schoenauer, M. (eds) Artificial Evolution. EA 2017. Lecture Notes in Computer Science(), vol 10764. Springer, Cham. https://doi.org/10.1007/978-3-319-78133-4_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-78133-4_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-78132-7
Online ISBN: 978-3-319-78133-4
eBook Packages: Computer ScienceComputer Science (R0)