rvGAHP: push-based job submission using reverse SSH connections

S. Callaghan, G. Juve, K. Vahi, P. Maechling, T. Jordan, E. Deelman
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引用次数: 6

Abstract

Computational science researchers running large-scale scientific workflow applications often want to run their workflows on the largest available compute systems to improve time to solution. Workflow tools used in distributed, heterogeneous, high performance computing environments typically rely on either a push-based or a pull-based approach for resource provisioning from these compute systems. However, many large clusters have moved to two-factor authentication for job submission, making traditional automated push-based job submission impossible. On the other hand, pull-based approaches such as pilot jobs may lead to increased complexity and a reduction in node-hour efficiency. In this paper, we describe a new, efficient approach based on HTCondor-G called reverse GAHP (rvGAHP) that allows us to push jobs using reverse SSH submissions with better efficiency than pull-based methods. We successfully used this approach to perform a large probabilistic seismic hazard analysis study using SCEC's CyberShake workflow in March 2017 on the Titan Cray XK7 hybrid system at Oak Ridge National Laboratory.
rvGAHP:使用反向SSH连接提交基于推送的作业
运行大规模科学工作流应用程序的计算科学研究人员通常希望在最大的可用计算系统上运行他们的工作流,以缩短解决方案的时间。在分布式、异构、高性能计算环境中使用的工作流工具通常依赖于从这些计算系统中提供资源的基于推送或基于拉的方法。然而,许多大型集群已经转向双因素身份验证来提交作业,这使得传统的基于推送的自动化作业提交变得不可能。另一方面,基于拉拔的方法,如先导作业,可能会增加复杂性,降低节点时间效率。在本文中,我们描述了一种基于HTCondor-G的新的高效方法,称为反向GAHP (rvGAHP),它允许我们使用反向SSH提交来推送作业,比基于拉的方法效率更高。2017年3月,我们在橡树岭国家实验室的Titan Cray XK7混合动力系统上成功地使用了SCEC的CyberShake工作流程,进行了大型概率地震危害分析研究。
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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