期刊
INTERNATIONAL JOURNAL OF DIGITAL EARTH
卷 12, 期 3, 页码 328-353出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/17538947.2018.1432709
关键词
Mean shift algorithm; GPU cluster; task scheduling; MPI; OpenCL
资金
- Engineering Research Center of Geospatial Information and Digital Technology (NASG) (Wuhan University) [SIDT20170601]
- Hubei Provincial Key Laboratory of Intelligent Geoinformation Processing (China University of Geosciences (Wuhan)) [KLIGIP2016A03]
- Fundamental Research Funds for the Central Universities [ZYGX2015J111]
- Key Laboratory of Spatial Data Mining & Information Sharing of the Ministry of Education (Fuzhou University) [2016LSDMIS06, 2017LSDMIS03]
- National Science Foundation of the United States [1251095, 1723292]
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [1251095, 1723292] Funding Source: National Science Foundation
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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