Abstract
This paper presents a parallel implementation of CURE, an efficient hierarchical data clustering algorithm, using the OpenMP programming model. OpenMP provides a means of transparent management of the asymmetry and non–determinism in CURE, while our OpenMP runtime support enables the effective exploitation of the irregular nested loop–level parallelism. Experimental results for various problem parameters demonstrate the scalability of our implementation and the effective utilization of parallel hardware, which enable the use of CURE for large data sets.
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Hadjidoukas, P.E., Amsaleg, L. (2008). Parallelization of a Hierarchical Data Clustering Algorithm Using OpenMP. In: Mueller, M.S., Chapman, B.M., de Supinski, B.R., Malony, A.D., Voss, M. (eds) OpenMP Shared Memory Parallel Programming. IWOMP 2005. Lecture Notes in Computer Science, vol 4315. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68555-5_24
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DOI: https://doi.org/10.1007/978-3-540-68555-5_24
Publisher Name: Springer, Berlin, Heidelberg
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