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

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.

@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},
  volume    = {8},
  number    = {12},
  year      = {2017},
  publisher = {The Science and Information Organization},
  author    = {Hicham Fakhi and Omar Bouattane and Mohamed Youssfi and Hassan Ouajji},
  doi       = {10.14569/IJACSA.2017.081221},
  url       = {https://doi.org/10.14569/IJACSA.2017.081221}
}