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DOI: 10.14569/IJACSA.2017.081221
PDF

Distributed GPU-Based K-Means Algorithm for Data-Intensive Applications: Large-Sized Image Segmentation Case

Author 1: Hicham Fakhi
Author 2: Omar Bouattane
Author 3: Mohamed Youssfi
Author 4: Hassan Ouajji

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 8 Issue 12, 2017.

  • Abstract and Keywords
  • How to Cite this Article
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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.

Keywords: Distributed computing; GPU computing; K-means; image segmentation

Hicham Fakhi, Omar Bouattane, Mohamed Youssfi and Hassan Ouajji, “Distributed GPU-Based K-Means Algorithm for Data-Intensive Applications: Large-Sized Image Segmentation Case” International Journal of Advanced Computer Science and Applications(IJACSA), 8(12), 2017. http://dx.doi.org/10.14569/IJACSA.2017.081221

@article{Fakhi2017,
title = {Distributed GPU-Based K-Means Algorithm for Data-Intensive Applications: Large-Sized Image Segmentation Case},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2017.081221},
url = {http://dx.doi.org/10.14569/IJACSA.2017.081221},
year = {2017},
publisher = {The Science and Information Organization},
volume = {8},
number = {12},
author = {Hicham Fakhi and Omar Bouattane and Mohamed Youssfi and Hassan Ouajji}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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