Abstract
Constraint Programming is one approach to declarative programming where a problem is modeled as a set of variables with a domain and a set of relations (constraints) between them. Constraint-based Local Search builds on the idea of using constraints to describe and control local search. Problems are modeled using constraints and heuristics for which solutions are searched, using Local Search. With the progressing move toward multi and many-core systems, parallelism has become mainstream as the number of cores continues to increase. Declarative programming approaches such as those based on constraints need to be better understood and experimented in order to understand their parallel behaviour. In this paper, we discuss experiments we have been carrying out with Adaptive Search and present a new parallel version of it based on GPI, a recent API and programming model for the development of scalable parallel applications. Our experiments on different problems show interesting speed-ups and, more importantly, a better understanding of how these gains are obtained, in the context of declarative programming.
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Machado, R., Abreu, S., Diaz, D. (2013). Parallel Performance of Declarative Programming Using a PGAS Model. In: Sagonas, K. (eds) Practical Aspects of Declarative Languages. PADL 2013. Lecture Notes in Computer Science, vol 7752. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45284-0_17
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DOI: https://doi.org/10.1007/978-3-642-45284-0_17
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