Abstract
State-of-the-art algorithms for solving hard computational problems often expose many parameters whose settings critically affect empirical performance. Manually exploring the resulting combinatorial space of parameter settings is often tedious and unsatisfactory. Automated approaches for finding good parameter settings are becoming increasingly prominent and have recently lead to substantial improvements in the state of the art for solving a variety of computationally challenging problems. However, running such automated algorithm configuration procedures is typically very costly, involving many thousands of invocations of the algorithm to be configured. Here, we study the extent to which parallel computing can come to the rescue. We compare straightforward parallelization by multiple independent runs with a more sophisticated method of parallelizing the model-based configuration procedure SMAC. Empirical results for configuring the MIP solver CPLEX demonstrate that near-optimal speedups can be obtained with up to 16 parallel workers, and that 64 workers can still accomplish challenging configuration tasks that previously took 2 days in 1–2 hours. Overall, we show that our methods make effective use of large-scale parallel resources and thus substantially expand the practical applicability of algorithm configuration methods.
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References
Hutter, F., Babić, D., Hoos, H.H., Hu, A.J.: Boosting verification by automatic tuning of decision procedures. In: Proc. of FMCAD 2007, pp. 27–34. IEEE Computer Society (2007)
Hutter, F., Hoos, H.H., Leyton-Brown, K.: Automated Configuration of Mixed Integer Programming Solvers. In: Lodi, A., Milano, M., Toth, P. (eds.) CPAIOR 2010. LNCS, vol. 6140, pp. 186–202. Springer, Heidelberg (2010)
Fawcett, C., Helmert, M., Hoos, H.H., Karpas, E., Röger, G., Seipp, J.: FD-Autotune: Domain-specific configuration using fast-downward. In: Proc. of ICAPS-PAL 2011, 8 p. (2011)
Hutter, F., Hoos, H.H., Leyton-Brown, K., Stützle, T.: ParamILS: an automatic algorithm configuration framework. Journal of Artificial Intelligence Research 36, 267–306 (2009)
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)
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)
Hoos, H.H., Stützle, T.: Local search algorithms for SAT: An empirical evaluation. Journal of Automated Reasoning 24(4), 421–481 (2000)
Gomes, C.P., Selman, B., Crato, N., Kautz, H.: Heavy-tailed phenomena in satisfiability and constraint satisfaction problems. Journal of Algorithms 24(1) (2000)
Ribeiro, C.C., Rosseti, I., Vallejos, R.: On the Use of Run Time Distributions to Evaluate and Compare Stochastic Local Search Algorithms. In: Stützle, T., Birattari, M., Hoos, H.H. (eds.) SLS 2009. LNCS, vol. 5752, pp. 16–30. Springer, Heidelberg (2009)
Hoos, H.H., Stützle, T.: Towards a characterisation of the behaviour of stochastic local search algorithms for SAT. Artificial Intelligence 112(1-2), 213–232 (1999)
Hutter, F.: Automated Configuration of Algorithms for Solving Hard Computational Problems. PhD thesis, University Of British Columbia, Department of Computer Science, Vancouver, Canada (October 2009)
López-Ibáñez, M., Dubois-Lacoste, J., Stützle, T., Birattari, M.: The irace package, iterated race for automatic algorithm configuration. Technical Report TR/IRIDIA/2011-004, IRIDIA, Université Libre de Bruxelles, Belgium (2011)
Jones, D.R.: A taxonomy of global optimization methods based on response surfaces. Journal of Global Optimization 21(4), 345–383 (2001)
Schonlau, M., Welch, W.J., Jones, D.R.: Global versus local search in constrained optimization of computer models. In: Flournoy, N., Rosenberger, W.F., Wong, W.K. (eds.) New Developments and Applications in Experimental Design, vol. 34, pp. 11–25. Institute of Mathematical Statistics, Hayward (1998)
Srinivas, N., Krause, A., Kakade, S., Seeger, M.: Gaussian process optimization in the bandit setting: No regret and experimental design. In: Proc. of ICML 2010 (2010)
Ginsbourger, D., Le Riche, R., Carraro, L.: Kriging Is Well-Suited to Parallelize Optimization. In: Tenne, Y., Goh, C.-K. (eds.) Computational Intel. in Expensive Opti. Prob. ALO, vol. 2, pp. 131–162. Springer, Heidelberg (2010)
Nell, C., Fawcett, C., Hoos, H.H., Leyton-Brown, K.: HAL: A Framework for the Automated Analysis and Design of High-Performance Algorithms. In: Coello, C.A.C. (ed.) LION 2011. LNCS, vol. 6683, pp. 600–615. Springer, Heidelberg (2011)
Balint, A., Diepold, D., Gall, D., Gerber, S., Kapler, G., Retz, R.: EDACC - An Advanced Platform for the Experiment Design, Administration and Analysis of Empirical Algorithms. In: Coello, C.A.C. (ed.) LION 2011. LNCS, vol. 6683, pp. 586–599. Springer, Heidelberg (2011)
Atamtürk, A.: On the facets of the mixed–integer knapsack polyhedron. Mathematical Programming 98, 145–175 (2003)
Atamtürk, A., Muñoz, J.C.: A study of the lot-sizing polytope. Mathematical Programming 99, 443–465 (2004)
Leyton-Brown, K., Pearson, M., Shoham, Y.: Towards a universal test suite for combinatorial auction algorithms. In: Proc. of EC 2000, pp. 66–76 (2000)
Gomes, C.P., van Hoeve, W.-J., Sabharwal, A.: Connections in Networks: A Hybrid Approach. In: Trick, M.A. (ed.) CPAIOR 2008. LNCS, vol. 5015, pp. 303–307. Springer, Heidelberg (2008)
Cote, M., Gendron, B., Rousseau, L.: Grammar-based integer programing models for multi-activity shift scheduling. Technical Report CIRRELT-2010-01, Centre interuniversitaire de recherche sur les réseaux d’entreprise, la logistique et le transport (2010)
Ahmadizadeh, K., Dilkina, B., Gomes, C.P., Sabharwal, A.: An Empirical Study of Optimization for Maximizing Diffusion in Networks. In: Cohen, D. (ed.) CP 2010. LNCS, vol. 6308, pp. 514–521. Springer, Heidelberg (2010)
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Hutter, F., Hoos, H.H., Leyton-Brown, K. (2012). Parallel Algorithm Configuration. In: Hamadi, Y., Schoenauer, M. (eds) Learning and Intelligent Optimization. LION 2012. Lecture Notes in Computer Science, vol 7219. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34413-8_5
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DOI: https://doi.org/10.1007/978-3-642-34413-8_5
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