Abstract
This paper presents an experimental study aimed at assessing the feasibility of parallelizing constraint propagation—with particular focus on arc-consistency—using Graphical Processing Units (GPUs). GPUs support a form of data parallelism that appears to be suitable to the type of processing required to cycle through constraints and domain values during consistency checking and propagation. The paper illustrates an implementation of a constraint solver capable of hybrid propagations (i.e., alternating CPU and GPU), and demonstrates the potential for competitiveness against sequential implementations.
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Campeotto, F., Dal Palù, A., Dovier, A., Fioretto, F., Pontelli, E. (2014). Exploring the Use of GPUs in Constraint Solving. In: Flatt, M., Guo, HF. (eds) Practical Aspects of Declarative Languages. PADL 2014. Lecture Notes in Computer Science, vol 8324. Springer, Cham. https://doi.org/10.1007/978-3-319-04132-2_11
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DOI: https://doi.org/10.1007/978-3-319-04132-2_11
Publisher Name: Springer, Cham
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