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.

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},
}