Citation: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 11, pages 445–452 (2017)
Abstract. In this paper we present MLTBiqCrunch, a hierarchically parallelized version of the open-source solver BiqCrunch. More precisely, this version has two levels of parallelization: a coarse grain, assigning a thread to a node evaluation and a fine grain, parallelizing a node evaluation when some threads are not busy. We present experiments on some classical binary quadratic optimization problems with comparison of their scalability and raw performance. In particular, we obtain a superlinear speedup for some of the most difficult instances.
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