Abstract
Multi-core processors have been broadly available to the public in the last five years. Parallelism has become a common design feature for computational intensive algorithms. In this paper we present a parallel implementation of an algorithm called interval constraint propagation for solution of constraint satisfaction problems over real numbers. Unlike existing implementations of this algorithm, our implementation scales well to many CPU cores with shared memory for sparse constraint satisfaction problems. We present scalability data for a quad-core processor on a number of benchmarks for non-linear constraint solvers.
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
Preview
Unable to display preview. Download preview PDF.
References
van Beek, P., Walsh, T.: Principles of Constraint Programming and Constraint Processing: A Review. AI Magazine 25(4) (2004)
Rolf, C.C., Kuchcinski, K.: Parallel Consistency in Constraint Programming. In: Proc. Int. Conf. on Parallel and Distributed Processing Techniques and Applications (PDPTA), pp. 638–644. CSREA Press (2009)
Vanderbei, R.: Cute AMPL models, http://www.orfe.princeton.edu/~rvdb/ampl/nlmodels/cute/
The Open Group and IEEE. POSIX Threads // IEEE Standard 1003.1. – The Open Group and IEEE (2004)
Richter, J., Nasarre, C.: Windows (R) via C/C++, 5th edn. Microsoft Press (2007) ISBN 9780735624245
Reinders, J.: Intel Threading Building Blocks, p. 336. O’Reilly Print (2007) ISBN 9780596514808
The OpenMP API specification for parallel programming, http://openmp.org
Blumofe, R.D., Joerg, C.F., Kuszmaul, B.C., Leiserson, C.E., Randall, K.H., Zhou, Y.: Cilk: An Efficient Multithreaded Runtime System. In: Proc. 5th ACM SIGPLAN Symp. on Principles and Practice of Parallel Programming (PPoPP), pp. 207–216 (1995)
Davis, E.: Constraint propagation with interval labels. J. Artificial Intelligence 32(3) (1987)
Granvilliers, L., Hains, G.: A conservative scheme for parallel interval narrowing. J. Inf. Process. Lett. 74(3-4), 141–146 (2000)
Beelitz, T., Bischof, C.H., Lang, B., Althoff, K.S.: Result-Verifying Solution of Nonlinear Systems in the Analysis of Chemical Processes. In: Alt, R., Frommer, A., Kearfott, R.B., Luther, W. (eds.) Dagstuhl Seminar 2003. LNCS, vol. 2991, pp. 198–205. Springer, Heidelberg (2004) ISBN 3540212604
Kasif, S.: On the parallel complexity of discrete relaxation in constraint satisfaction networks. J. Artif. Intel. 45(3), 99–118 (1990)
Bordeaux, L., Hamadi, Y., Samulowitz, H.: Experiments with Massively Parallel Constraint Solving. In: Proc. Int. Joint Conf. on Artif. Intel., pp. 443–448 (2009)
Kalinnik, N., Schubert, T., Ábrahám, E., Wimmer, R., Becker, B.: Picoso - A Parallel Interval Constraint Solver. In: Proc. Int. Conf. on Parallel and Distributed Processing Techniques and Applications (PDPTA), pp. 473–479. CSREA Press (2009)
Rohn, J., Kreinovich, V.: Computing exact componentwise bounds on solutions of linear systems with interval data is NP-hard. SIAM J. Matr. Anal. Appl. 16, 415–420 (1995)
Cann, D.: Retire Fortran?: a debate rekindled. Communications of the ACM 35(8) (1992)
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
Petrov, E. (2012). Scalable Parallel Interval Propagation for Sparse Constraint Satisfaction Problems. In: Clarke, E., Virbitskaite, I., Voronkov, A. (eds) Perspectives of Systems Informatics. PSI 2011. Lecture Notes in Computer Science, vol 7162. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29709-0_26
Download citation
DOI: https://doi.org/10.1007/978-3-642-29709-0_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29708-3
Online ISBN: 978-3-642-29709-0
eBook Packages: Computer ScienceComputer Science (R0)