Abstract
In practice, combinatorial optimization problems are complex and computationally time-intensive. Local search algorithms are powerful heuristics which allow to significantly reduce the computation time cost of the solution exploration space. In these algorithms, the multi-start model may improve the quality and the robustness of the obtained solutions. However, solving large size and time-intensive optimization problems with this model requires a large amount of computational resources. GPU computing is recently revealed as a powerful way to harness these resources. In this paper, the focus is on the multi-start model for local search algorithms on GPU. We address its re-design, implementation and associated issues related to the GPU execution context. The preliminary results demonstrate the effectiveness of the proposed approaches and their capabilities to exploit the GPU architecture.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Talbi, E.G.: Metaheuristics: From design to implementation. Wiley, Chichester (2009)
Van Luong, T., Melab, N., Talbi, E.-G.: Local search algorithms on graphics processing units. A case study: The permutation perceptron problem. In: Cowling, P., Merz, P. (eds.) EvoCOP 2010. LNCS, vol. 6022, pp. 264–275. Springer, Heidelberg (2010)
Luong, T.V., Melab, N., Talbi, E.G.: Large neighborhood for local search algorithms. In: IPDPS. IEEE Computer Society, Los Alamitos (2010)
Zhu, W., Curry, J., Marquez, A.: Simd tabu search with graphics hardware acceleration on the quadratic assignment problem. International Journal of Production Research (2008)
Janiak, A., Janiak, W.A., Lichtenstein, M.: Tabu search on gpu. J. UCS 14(14), 2416–2426 (2008)
Alba, E., Talbi, E.G., Luque, G., Melab, N.: 4. Metaheuristics and Parallelism. In: Parallel Metaheuristics: A New Class of Algorithms, pp. 79–104. Wiley, Chichester (2005)
Zomaya, A.Y., Patterson, D., Olariu, S.: Sequential and parallel meta-heuristics for solving the single row routing problem. Cluster Computing 7(2), 123–139 (2004)
Melab, N., Cahon, S., Talbi, E.G.: Grid computing for parallel bioinspired algorithms. J. Parallel Distributed Computing 66(8), 1052–1061 (2006)
Ryoo, S., Rodrigues, C.I., Stone, S.S., Stratton, J.A., Ueng, S.Z., Baghsorkhi, S.S., Hwu, M.W.: Program optimization carving for gpu computing. J. Parallel Distribributed Computing 68(10), 1389–1401 (2008)
NVIDIA: CUDA Programming Guide Version 3.0 (2010)
Group, K.: OpenCL 1.0 Quick Reference Card (2010)
Burkard, R.E., Deineko, V.G., Woeginger, G.J.: The travelling salesman problem on permuted monge matrices. J. Comb. Optim. 2(4), 333–350 (1998)
Bader, D.A., Sachdeva, V.: A cache-aware parallel implementation of the push-relabel network flow algorithm and experimental evaluation of the gap relabeling heuristic. In: Oudshoorn, M.J., Rajasekaran, S. (eds.) ISCA PDCS, ISCA, pp. 41–48 (2005)
Nickolls, J., Buck, I., Garland, M., Skadron, K.: Scalable parallel programming with cuda. ACM Queue 6(2), 40–53 (2008)
Dell’Amico, M., Trubian, M.: Applying tabu search to the job-shop scheduling problem. Ann. Oper. Res. 41(1-4), 231–252 (1993)
NVIDIA: GPU Gems 3. Chapter 37: Efficient Random Number Generation and Application Using CUDA (2010)
Taillard, É.D.: Robust taboo search for the quadratic assignment problem. Parallel Computing 17(4-5), 443–455 (1991)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Van Luong, T., Melab, N., Talbi, EG. (2011). GPU-Based Multi-start Local Search Algorithms. In: Coello, C.A.C. (eds) Learning and Intelligent Optimization. LION 2011. Lecture Notes in Computer Science, vol 6683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25566-3_24
Download citation
DOI: https://doi.org/10.1007/978-3-642-25566-3_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25565-6
Online ISBN: 978-3-642-25566-3
eBook Packages: Computer ScienceComputer Science (R0)