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Utilising Amazon web services to provide an on demand urgent computing facility for climateprediction.net

机译:利用Amazon Web服务为Climateprediction.net提供按需的紧急计算功能

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climateprediction.net has traditionally been an activity that requires a large amount of computing resources from its volunteer network, whilst allowing a time-frame of weeks to months for simulations to be returned for each project. However, there is an increasing trend of projects requiring results in shorter and shorter timescales. Under no project is this clearer than in the World Weather Attribution (WWA) initiative, where we are aiming to provide in near to real-time an answer to how anthropogenic climate change has altered the frequency of occurrence of a particular type of extreme weather event, either as it happens or as soon after as is practical. As such we need the ability to run simulations on alternate resources when volunteer resources will not provide results within the necessary timeframe. This paper describes a workflow to distribute ensembles of climateprediction.net simulations in the Amazon Elastic Compute Cloud, to provide urgent compute capability for projects such as WWA. We propose a method of optimizing the use of cloud resources to minimize cost while maximising throughput. A case study is presented to provide a proof of concept of this methodology. As such, this is a clear example of beneficial utilisation of cloud resources to supplement those available through our volunteer community.
机译:传统上,climateprediction.net是一项活动,需要其志愿者网络提供大量计算资源,同时允许数周至数月的时间范围内每个项目的模拟返回。但是,有越来越多的项目要求在越来越短的时间范围内取得成果的趋势。在任何项目下,这都比世界天气归因(WWA)计划要清晰得多。在该计划中,我们旨在实时提供有关人为气候变化如何改变特定类型极端天气事件发生频率的答案。 ,无论是实际发生还是在实际发生后尽快发生。因此,当志愿人员资源无法在必要的时间范围内提供结果时,我们需要能够在备用资源上运行模拟的能力。本文介绍了一种工作流,用于在Amazon Elastic Compute Cloud中分发climateprediction.net模拟的集合,从而为WWA等项目提供紧急计算功能。我们提出一种优化云资源使用的方法,以最大程度地降低成本,同时最大程度地提高吞吐量。提出了一个案例研究,以提供这种方法论的概念证明。因此,这是有益地利用云资源来补充可通过我们的志愿者社区获得的云资源的明显示例。

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