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首页> 外文期刊>Journal of the American Water Resources Association >A COMPREHENSIVE PYTHON TOOLKIT FOR ACCESSING HIGH-THROUGHPUT COMPUTING TO SUPPORT LARGE HYDROLOGIC MODELING TASKS
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A COMPREHENSIVE PYTHON TOOLKIT FOR ACCESSING HIGH-THROUGHPUT COMPUTING TO SUPPORT LARGE HYDROLOGIC MODELING TASKS

机译：全面的Python工具包，可访问高吞吐量计算以支持大型水文建模任务

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摘要

The National Flood Interoperability Experiment (NFIE) was an undertaking that initiated a transformation in national hydrologic forecasting by providing streamflow forecasts at high spatial resolution over the whole country. This type of large-scale, high-resolution hydrologic modeling requires flexible and scalable tools to handle the resulting computational loads. While high-throughput computing (HTC) and cloud computing provide an ideal resource for large-scale modeling because they are cost-effective and highly scalable, nevertheless, using these tools requires specialized training that is not always common for hydrologists and engineers. In an effort to facilitate the use of HTC resources the National Science Foundation (NSF) funded project, CI-WATER, has developed a set of Python tools that can automate the tasks of provisioning and configuring an HTC environment in the cloud, and creating and submitting jobs to that environment. These tools are packaged into two Python libraries: CondorPy and TethysCluster. Together these libraries provide a comprehensive toolkit for accessing HTC to support hydrologic modeling. Two use cases are described to demonstrate the use of the toolkit, including a web app that was used to support the NFIE national-scale modeling.
机译：国家洪水互操作性实验（NFIE）是一项旨在通过在全国范围内提供具有高空间分辨率的水流预报来启动国家水文预报的变革的事业。这种大规模，高分辨率的水文建模需要灵活且可扩展的工具来处理由此产生的计算负荷。尽管高通量计算（HTC）和云计算由于具有成本效益和高度可伸缩性而为大规模建模提供了理想的资源，但是，使用这些工具需要专门的培训，而培训对水文学家和工程师而言并不总是如此。为了促进HTC资源的使用，美国国家科学基金会（NSF）资助的项目CI-WATER开发了一套Python工具，这些工具可以自动完成在云中配置和配置HTC环境以及创建和创建HTC环境的任务。向该环境提交工作。这些工具打包在两个Python库中：CondorPy和TethysCluster。这些库一起提供了用于访问HTC以支持水文建模的综合工具包。描述了两个用例来演示该工具包的用法，包括一个用于支持NFIE国家级建模的Web应用程序。

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