Distributed GPU-Based K-Means Algorithm for Data-Intensive Applications: Large-Sized Image Segmentation Case
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2017, Vol 8, Issue 12
Abstract
K-means is a compute-intensive iterative algorithm. Its use in a complex scenario is cumbersome, specifically in data-intensive applications. In order to accelerate the K-means running time for data-intensive application, such as large sized image segmentation, we use a distributed multi-agent system accelerated by GPUs. In this K-means version, the input image data are divided into subsets of image data which can be performed independently on GPUs. In each GPU, we offloaded the data assignment and the K-centroids recalculation steps of the K-means algorithm for a massively parallel processing. We have implemented this K-means version on the Nvidia GPU with Compute Unified Device Architecture. The distributed multi-agent system was written with Java Agent Development framework.
Authors and Affiliations
Hicham Fakhi, Omar Bouattane, Mohamed Youssfi, Hassan Ouajji
Keywords
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- EP ID EP258335
- DOI 10.14569/IJACSA.2017.081221
- Views 106
- Downloads 0
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