Implementation of the parallel mean shift-based image segmentation algorithm on a GPU cluster |
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| Authors: | Fang Huang Yinjie Chen Li Li Ji Zhou Xicheng Tan |
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| Institution: | 1. School of Resources &2. Environment, University of Electronic Science and Technology of China, Chengdu, People’s Republic of China;3. Institute of Remote Sensing Big Data, Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, People’s Republic of China;4. School of Construction &5. Management Engineering, Xihua University, Chengdu, People’s Republic of China;6. International School of Software, Wuhan University, Wuhan, People’s Republic of China |
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| Abstract: | The mean shift image segmentation algorithm is very computation-intensive. To address the need to deal with a large number of remote sensing (RS) image segmentations in real-world applications, this study has investigated the parallelization of the mean shift algorithm on a single graphics processing unit (GPU) and a task-scheduling method with message passing interface (MPI)+OpenCL programming model on a GPU cluster platform. This paper presents the test results of the parallel mean shift image segmentation algorithm on Shelob, a GPU cluster platform at Louisiana State University, with different datasets and parameters. The experimental results show that the proposed parallel algorithm can achieve good speedups with different configurations and RS data and can provide an effective solution for RS image processing on a GPU cluster. |
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| Keywords: | Mean shift algorithm GPU cluster task scheduling MPI OpenCL |
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