CP 2014: Principles and Practice of Constraint Programming pp 622-635 | Cite as
Improvement of the Embarrassingly Parallel Search for Data Centers
Conference paper
Abstract
We propose an adaptation of the Embarrassingly Parallel Search (EPS) method for data centers. EPS is a simple but efficient method for parallel solving of CSPs. EPS decomposes the problem in many distinct subproblems which are then solved independently by workers. EPS performed well on multi-cores machines (40), but some issues arise when using more cores in a datacenter. Here, we identify the decomposition as the cause of the degradation and propose a parallel decomposition to address this issue. Thanks to it, EPS gives almost linear speedup and outperforms work stealing by orders of magnitude using the Gecode solver.
Keywords
Data Center Resolution Time Constraint Programming Initial Problem Decomposition Algorithm
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Preview
Unable to display preview. Download preview PDF.
References
- 1.Gecode 4.0.0 (2012), http://www.gecode.org/
- 2.Warren Burton, F., Ronan Sleep, M.: Executing functional programs on a virtual tree of processors. In: Proceedings of the 1981 Conference on Functional Programming Languages and Computer Architecture, FPCA 1981, pp. 187–194. ACM, New York (1981)CrossRefGoogle Scholar
- 3.Chu, G., Schulte, C., Stuckey, P.J.: Confidence-Based Work Stealing in Parallel Constraint Programming. In: Gent, I.P. (ed.) CP 2009. LNCS, vol. 5732, pp. 226–241. Springer, Heidelberg (2009)CrossRefGoogle Scholar
- 4.Halstead Jr., R.H.: Implementation of multilisp: Lisp on a multiprocessor. In: Proceedings of the 1984 ACM Symposium on LISP and Functional Programming, LFP 1984, pp. 9–17. ACM, New York (1984)CrossRefGoogle Scholar
- 5.Jaffar, J., Santosa, A.E., Yap, R.H.C., Zhu, K.Q.: Scalable Distributed Depth-First Search with Greedy Work Stealing. In: ICTAI, pp. 98–103. IEEE Computer Society (2004)Google Scholar
- 6.Kjellerstrand, H. (2014), http://www.hakank.org/
- 7.Menouer, T., Le Cun, B., Vander-Swalmen, P.: Partitioning methods to parallelize constraint programming solver using the parallel framework bobpp. In: Nguyen, N.T., van Do, T., Thi, H.A. (eds.) ICCSAMA 2013. SCI, vol. 479, pp. 117–127. Springer, Heidelberg (2013)CrossRefGoogle Scholar
- 8.Michel, L., See, A., Hentenryck, P.V.: Transparent Parallelization of Constraint Programming. INFORMS Journal on Computing 21(3), 363–382 (2009)CrossRefMATHGoogle Scholar
- 9.MiniZinc (2012), http://www.g12.csse.unimelb.edu.au/minizinc/
- 10.Nielsen, M.: Parallel search in gecode. PhD thesis, Masters thesis, KTH Royal Institute of Technology (2006)Google Scholar
- 11.Pedro, V., Abreu, S.: Distributed Work Stealing for Constraint Solving. CoRR, abs/1009.3800:1–18 (2010)Google Scholar
- 12.Perron, L.: Search Procedures and Parallelism in Constraint Programming. In: Jaffar, J. (ed.) CP 1999. LNCS, vol. 1713, pp. 346–361. Springer, Heidelberg (1999)CrossRefGoogle Scholar
- 13.Régin, J.-C., Rezgui, M., Malapert, A.: Embarrassingly parallel search. In: Schulte, C. (ed.) CP 2013. LNCS, vol. 8124, pp. 596–610. Springer, Heidelberg (2013)CrossRefGoogle Scholar
- 14.Schulte, C.: Parallel Search Made Simple. In: Proceedings of TRICS: Techniques for Implementing Constraint programming Systems, a post-conference Workshop of CP 2000, pp. 41–57 (2000)Google Scholar
- 15.Xie, F., Davenport, A.: Massively parallel constraint programming for supercomputers: Challenges and initial results. In: Lodi, A., Milano, M., Toth, P. (eds.) CPAIOR 2010. LNCS, vol. 6140, pp. 334–338. Springer, Heidelberg (2010)CrossRefGoogle Scholar
- 16.Zoeteweij, P., Arbab, F.: A Component-Based Parallel Constraint Solver. In: De Nicola, R., Ferrari, G.-L., Meredith, G. (eds.) COORDINATION 2004. LNCS, vol. 2949, pp. 307–322. Springer, Heidelberg (2004)CrossRefGoogle Scholar
Copyright information
© Springer International Publishing Switzerland 2014