Exploring Cloud Computing for Large-Scale Scientific Applications

Guang Lin, Binh Han, Jian Yin, I. Gorton
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引用次数: 10

Abstract

This paper explores cloud computing for large-scale data intensive scientific applications. Cloud computing is attractive because it provides hardware and software resources on-demand, which relieves the burden of acquiring and maintaining a huge amount of resources that may be used only once by a scientific application. However, unlike typical commercial applications that often just requires a moderate amount of ordinary resources, large-scale scientific applications often need to process enormous amount of data in the terabyte or even petabyte range and require special high performance hardware with low latency connections to complete computation in a reasonable amount of time. To address these challenges, we build an infrastructure that can dynamically select high performance computing hardware across institutions and dynamically adapt the computation to the selected resources to achieve high performance. We have also demonstrated the effectiveness of our infrastructure by building a system biology application and an uncertainty quantification application for carbon sequestration, which can efficiently utilize data and computation resources across several institutions.
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探索大规模科学应用的云计算
本文探讨了大规模数据密集型科学应用中的云计算。云计算之所以具有吸引力,是因为它按需提供硬件和软件资源,从而减轻了获取和维护科学应用程序可能只使用一次的大量资源的负担。然而,与通常只需要适量普通资源的典型商业应用程序不同,大规模科学应用程序通常需要处理tb甚至pb范围内的大量数据,并且需要具有低延迟连接的特殊高性能硬件才能在合理的时间内完成计算。为了应对这些挑战,我们构建了一个基础设施,它可以动态地跨机构选择高性能计算硬件,并动态地使计算适应所选资源,以实现高性能。我们还通过构建系统生物学应用程序和碳封存的不确定性量化应用程序来证明我们的基础设施的有效性,这些应用程序可以有效地利用多个机构的数据和计算资源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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