Implementation of the parallel mean shift-based image segmentation algorithm on a GPU cluster
Implementation of the parallel mean shift-based image segmentation algorithm on a GPU cluster
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DOI：
10.1080/17538947.2018.1432709
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发表时间：
2019-03-04
影响因子：
5.1
通讯作者：
Fan, Guangsong
中科院分区：
文献类型：
--
作者：
Huang, Fang;Chen, Yinjie;Fan, Guangsong
均值漂移图像分割算法是一种计算量很大的算法。针对实际应用中大量遥感图像分割的需要，研究了均值漂移算法在单个图形处理器（GPU）上的并行化问题，以及在GPU集群平台上采用消息传递接口（MPI）+OpenCL编程模型的任务调度方法。本文介绍了并行均值漂移图像分割算法在路易斯安那州立大学的GPU集群平台Shelob上的测试结果，不同的数据集和参数。实验结果表明，该并行算法在不同配置和RS数据下均能获得较好的加速比，为GPU集群上的RS图像处理提供了一种有效的解决方案。
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.