Abstract
Simulated Annealing (SA) is a powerful global optimization technique that is frequently used for solving many practical problems from various scientific and technical fields. In this article we present a novel approach to parallelization of SA and propose an algorithm optimized for execution in GPU clusters. Our technique exploits the basic characteristics of such environments by using hierarchical problem decomposition. The proposed algorithm performs especially well for complex problems with large number of variables. We compare our approach with traditional parallel Simulated Annealing, both in terms of speed and result accuracy.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Boissin, N., Lutton, J.-L.: A parallel simulated annealing algorithm. Parallel Computing 19(8), 859–872 (1993)
Choong, A., Beidas, R., Zhu, J.: Parallelizing Simulated Annealing-Based Placement Using GPGPU. In: Proceedings of the 2010 International Conference on Field Programmable Logic and Applications, pp. 31–34 (2010)
Debudaj-Grabysz, A., Czech, Z.: Theoretical and Practical Issues of Parallel Simulated Annealing. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Wasniewski, J. (eds.) PPAM 2007. LNCS, vol. 4967, pp. 189–198. Springer, Heidelberg (2008)
Frost, R., Heineman, P.: Simulated annealing: A heuristic for parallel stochastic optimization. Tech. rep., San Diego Supercomputer Center (1997)
Grama, A., Gupta, A., Karypis, G., Kumar, V.: Introduction to Parallel Computing, 2nd edn. Addison Wesley, Harlow (2003)
Greening, D.R.: Parallel simulated annealing techniques. Physica 42, 293–306 (1990)
Han, Y., Roy, S., Chakraborty, K.: Optimizing simulated annealing on GPU: A case study with IC floorplanning. In: Proceedings of the 12th International Symposium on Quality Electronic Design, pp. 1–7 (2011)
Ingber, L.: Simulated annealing: Practice versus theory. Mathematical Computer Modelling 18(11), 29–57 (1993)
Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)
Matsumoto, M., Nishimura, T.: Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator. ACM Trans. Model. Comput. Simul. 8(1), 3–30 (1998)
Molga, M., Smutnicki, C.: Test functions for optimization needs (2005), http://www.zsd.ict.pwr.wroc.pl/files/docs/functions.pdf
NVIDIA: CUDA C programming guide (2010), http://developer.download.nvidia.com/compute/cuda/3_2_prod/toolkit/docs/CUDA_C_Programming_Guide.pdf
NVIDIA: CUDA CURAND library (2010), http://developer.download.nvidia.com/compute/cuda/3_2_prod/toolkit/docs/CURAND_Library.pdf
Özdamar, L., Demirhan, M.: Experiments with new stochastic global optimization search techniques. Comput. Oper. Res. 27, 841–865 (2000)
Onbaşoğlu, E., Özdamar, L.: Parallel simulated annealing algorithms in global optimization. Journal of Global Optimization 19, 27–50 (2001)
Ryoo, S., Rodrigues, C., Stone, S., et al.: Program optimization carving for GPU computing. Journal of Parallel and Distributed Computing 68(10), 1389–1401 (2008)
Sosnowski, J., Tymoczko, A., Gawkowski, P.: An Approach to Distributed Fault Injection Experiments. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Wasniewski, J. (eds.) PPAM 2007. LNCS, vol. 4967, pp. 361–370. Springer, Heidelberg (2008)
Thomas, D.B., Luk, W.: GPU optimised uniform random number generation, http://www.doc.ic.ac.uk/~dt10/research/gpu_rng/gpu_warp_rng.pdf
Verhoeven, M., Aarts, E.: Parallel local search. Journal of Heuristics 1, 43–65 (1995)
Zbierski, M.: Analysis of a CUDA-based distributed system in the context of selected Monte Carlo methods. Master’s thesis, Warsaw University of Technology (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zbierski, M. (2012). A Simulated Annealing Algorithm for GPU Clusters. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Waśniewski, J. (eds) Parallel Processing and Applied Mathematics. PPAM 2011. Lecture Notes in Computer Science, vol 7203. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31464-3_76
Download citation
DOI: https://doi.org/10.1007/978-3-642-31464-3_76
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31463-6
Online ISBN: 978-3-642-31464-3
eBook Packages: Computer ScienceComputer Science (R0)