This work presents a GPU-based backtracking algorithm for permutation combinatorial problems based on the Integer-Vector-Matrix (IVM) data structure. IVM is a data structure dedicated to permutation combinatorial optimization problems. In this algorithm, the load balancing is performed without intervention of the CPU, inside a work stealing phase invoked after each node expansion phase. The proposed work stealing approach uses a virtual n-dimensional hypercube topology and a triggering mechanism to reduce the overhead incurred by dynamic load balancing. We have implemented this new algorithm for solving instances of the Asymmetric Travelling Salesman Problem by implicit enumeration, a scenario where the cost of node evaluation is low, compared to the overall search procedure. Experimental results show that the dynamically load balanced IVM-algorithm reaches speed-ups up to 17× over a serial implementation using a bitset-data structure and up to 2× over its GPU counterpart.
CITATION STYLE
Pessoa, T. C., Gmys, J., Melab, N., de Carvalho Junior, F. H., & Tuyttens, D. (2016). A GPU-based backtracking algorithm for permutation combinatorial problems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10048 LNCS, pp. 310–324). Springer Verlag. https://doi.org/10.1007/978-3-319-49583-5_24
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