Abstract
Applying parallelism to constraint solving seems a promising approach and it has been done with varying degrees of success. Early attempts to parallelize constraint propagation, which constitutes the core of traditional interleaved propagation and search constraint solving, were hindered by its essentially sequential nature. Recently, parallelization efforts have focussed mainly on the search part of constraint solving, as well as on local-search based solving. Lately, a particular source of parallelism has become pervasive, in the guise of GPUs, able to run thousands of parallel threads, and they have naturally drawn the attention of researchers in parallel constraint solving.
In this paper, we address challenges faced when using multiple devices for constraint solving, especially GPUs, such as deciding on the appropriate level of parallelism to employ, load balancing and inter-device communication, and present our current solutions.
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Notes
- 1.
Intel MICs are coprocessors that combine many Intel processor cores onto a single chip with dedicated RAM.
- 2.
If a device takes less than one second to explore a block of search spaces, most of that time is spent communicating with the host and initializing its data structures.
- 3.
A CUDA core is a processing element capable of executing one integer or floating instruction per clock for a thread.
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Acknowledgments
This work was partially funded by Fundação para a Ciência e Tecnologia (FCT) under grant UID/CEC/4668/2016 (LISP). Some of the experimentation was carried out on the khromeleque cluster of the University of Évora, which was partly funded by grants ALENT-07-0262-FEDER-001872 and ALENT-07-0262-FEDER-001876.
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Roque, P., Pedro, V. (2018). Constraint Solving on Hybrid Systems. In: Seipel, D., Hanus, M., Abreu, S. (eds) Declarative Programming and Knowledge Management. WFLP WLP INAP 2017 2017 2017. Lecture Notes in Computer Science(), vol 10997. Springer, Cham. https://doi.org/10.1007/978-3-030-00801-7_1
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