用Python实现HPC工作流的动态配置和执行

Chris Harris, P. O’leary, M. Grauer, Aashish Chaudhary, Chris Kotfila, Robert M. O'Bara
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引用次数: 3

摘要

在过去的几十年里,高性能计算(HPC)工作流已经被证明有助于理解科学现象,并以更快的速度和更低的成本生产更好的产品。然而,由于各种原因,HPC工作流难以实现和使用。在本文中,我们描述了基于python的积云的发展,它解决了许多这些障碍。cumulus是一个用于动态配置和执行HPC工作流的平台。cumulus提供了构建应用程序所需的基础设施,这些应用程序可以在其工作流中利用传统或基于云的HPC资源。最后,我们演示了在web和桌面模拟应用程序以及基于Apache spark的分析应用程序中使用积云。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dynamic Provisioning and Execution of HPC Workflows Using Python
High-performance computing (HPC) workflows over the last several decades have proven to assist in the understanding of scientific phenomena and the production of better products, more quickly, and at reduced cost. However, HPC workflows are difficult to implement and use for a variety of reasons. In this paper, we describe the development of the Python-based cumulus, which addresses many of these barriers. cumulus is a platform for the dynamic provisioning and execution of HPC workflows. cumulus provides the infrastructure needed to build applications that leverage traditional or Cloud-based HPC resources in their workflows. Finally, we demonstrate the use of cumulus in both web and desktop simulation applications, as well as in an Apache Spark-based analysis application.
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