Abstract
Probabilistic frequent itemset mining, which discovers frequent itemsets from uncertain data, has attracted much attention due to inherent uncertainty in the real world. Many algorithms have been proposed to tackle this problem, but their performance is not satisfactory because handling uncertainty incurs high processing cost. To accelerate such computation, we utilize GPUs (Graphics Processing Units). Our previous work accelerated an existing algorithm with a single GPU. In this paper, we extend the work to employ multiple GPUs. Proposed methods minimize the amount of data that need to be communicated among GPUs, and achieve load balancing as well. Based on the methods, we also present algorithms on a GPU cluster. Experiments show that the single-node methods realize near-linear speedups.
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Kozawa, Y., Amagasa, T., Kitagawa, H. (2013). Parallel and Distributed Mining of Probabilistic Frequent Itemsets Using Multiple GPUs. In: Decker, H., Lhotská, L., Link, S., Basl, J., Tjoa, A.M. (eds) Database and Expert Systems Applications. DEXA 2013. Lecture Notes in Computer Science, vol 8055. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40285-2_14
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DOI: https://doi.org/10.1007/978-3-642-40285-2_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40284-5
Online ISBN: 978-3-642-40285-2
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