Comparing Workflow Application Designs for High Resolution Satellite Image Analysis
Comparing Workflow Application Designs for High Resolution Satellite Image Analysis
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DOI：
10.1016/j.future.2021.04.023
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发表时间：
2020-10
期刊：
影响因子：
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通讯作者：
Aymen Al-Saadi;Ioannis Paraskevakos;B. Gonçalves;H. Lynch;S. Jha;M. Turilli
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文献类型：
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作者：
Aymen Al-Saadi;Ioannis Paraskevakos;B. Gonçalves;H. Lynch;S. Jha;M. Turilli
利用甚高分辨率卫星和航空影像对生态系统进行监测和大规模调查。卷积神经网络已经成功地用于分析这些图像，以检测大型动物和显著特征。随着数据集的体积和图像数量的增加，利用高性能计算资源变得必要。在本文中，我们研究了三种任务并行、数据驱动的工作流设计，以支持高性能计算平台上具有异构任务的图像分析管道。我们分析了每种设计在处理3097和1575张图像时的能力，用于两个不同的用例，总共4,672张卫星和航空图像以及8.35 TB的数据。我们对图像处理流水线任务的执行时间进行了实验建模。我们执行实验来表征资源利用率、完成总时间和每个设计的开销。我们的分析显示了哪种设计最适合具有相似特征的科学管道。
Very High Resolution satellite and aerial imagery are used to monitor and conduct large scale surveys of ecological systems. Convolutional Neural Networks have successfully been employed to analyze such imagery to detect large animals and salient features. As the datasets increase in volume and number of images, utilizing High Performance Computing resources becomes necessary. In this paper, we investigate three task-parallel, data-driven workflow designs to support imagery analysis pipelines with heterogeneous tasks on high performance computing platforms. We analyze the capabilities of each design when processing 3097 and 1575 images for two distinct use cases, for a total of 4,672 satellite and aerial images and 8.35 TB of data. We experimentally model the execution time of the tasks of the image processing pipelines. We perform experiments to characterize resource utilization, total time to completion and overheads of each design. Our analysis shows which design is best suited to scientific pipelines with similar characteristics.