Job Coscheduling on Coupled High-End Computing Systems

Wei Tang, N. Desai, V. Vishwanath, Daniel Buettner, Z. Lan
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引用次数: 4

Abstract

Supercomputer centers often deploy large-scale computing systems together with an associated data analysis or visualization system. In this paper, we propose a co scheduling mechanism, providing the ability to coordinate execution between jobs on different systems. The mechanism is built on top of a lightweight protocol for coordination between policy domains without manual intervention. We have evaluated this system using real job traces from Intrepid and Eureka, the production Blue Gene/P and data analysis systems, respectively, deployed at Argonne National Laboratory. Our experimental results quantify the costs of co scheduling and demonstrate that co scheduling can be achieved with limited impact on system performance under varying workloads.
耦合高端计算系统的作业协同调度
超级计算机中心通常部署大型计算系统以及相关的数据分析或可视化系统。在本文中,我们提出了一种协同调度机制,提供了在不同系统上的作业之间协调执行的能力。该机制构建在轻量级协议之上,用于策略域之间的协调,无需人工干预。我们利用部署在Argonne国家实验室的Intrepid和Eureka生产蓝色基因/P和数据分析系统的真实作业轨迹对该系统进行了评估。我们的实验结果量化了协同调度的成本,并证明了在不同工作负载下,协同调度可以在对系统性能影响有限的情况下实现。
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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