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Proceedings Paper

Performance impact of dynamic parallelism on different clustering algorithms
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Paper Abstract

In this paper, we aim to quantify the performance gains of dynamic parallelism. The newest version of CUDA, CUDA 5, introduces dynamic parallelism, which allows GPU threads to create new threads, without CPU intervention, and adapt to its data. This effectively eliminates the superfluous back and forth communication between the GPU and CPU through nested kernel computations. The change in performance will be measured using two well-known clustering algorithms that exhibit data dependencies: the K-means clustering and the hierarchical clustering. K-means has a sequential data dependence wherein iterations occur in a linear fashion, while the hierarchical clustering has a tree-like dependence that produces split tasks. Analyzing the performance of these data-dependent algorithms gives us a better understanding of the benefits or potential drawbacks of CUDA 5’s new dynamic parallelism feature.

Paper Details

Date Published: 29 May 2013
PDF: 8 pages
Proc. SPIE 8752, Modeling and Simulation for Defense Systems and Applications VIII, 87520E (29 May 2013); doi: 10.1117/12.2018069
Show Author Affiliations
Jeffrey DiMarco, Univ. of Delaware (United States)
Michela Taufer, Univ. of Delaware (United States)


Published in SPIE Proceedings Vol. 8752:
Modeling and Simulation for Defense Systems and Applications VIII
Eric J. Kelmelis, Editor(s)

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