Parallel and Reconfigurable Mesh Architecture for Low and Medium Level Image Processing Applications

Ihirri, Soukaina; Errami, Ahmed; Mohamed, Khaldoun

doi:10.1007/978-981-10-1627-1_42

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2017

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“…This obvious need has led naturally to more powerful computers to allow image processing researchers to use new High-Performance Computing (HPC) strategies based on the parallelism and distributed approaches such as 2D or 3D reconfigurable mesh [10], FPGA, and recently GPU [8], [9] and Hadoop.…”

Section: Introductionmentioning

confidence: 99%

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

Fakhi¹,

Bouattane²,

Youssfi³

et al. 2017

ijacsa

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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 multiagent system was written with Java Agent Development framework.

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Section: Introductionmentioning

confidence: 99%