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.
This work was granted access to the HPC and visualization resources of ”Centre de Calcul Interactif” hosted by the University of Nice Sophia Antipolis. It was also partially supported by OSEO, with the project ISI ”Pajero”.
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Régin, JC., Rezgui, M., Malapert, A. (2014). Improvement of the Embarrassingly Parallel Search for Data Centers. In: O’Sullivan, B. (eds) Principles and Practice of Constraint Programming. CP 2014. Lecture Notes in Computer Science, vol 8656. Springer, Cham. https://doi.org/10.1007/978-3-319-10428-7_45
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DOI: https://doi.org/10.1007/978-3-319-10428-7_45
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