为大型计算设施提供全面的数据驱动系统管理

J. Browne, R. L. Deleon, Charng-Da Lu, Matthew D. Jones, S. Gallo, Amin Ghadersohi, A. Patra, W. Barth, John L. Hammond, T. Furlani, R. McLay
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引用次数: 21

摘要

本文提出了一个基于开源工具TACC_Stats的工具链,用于对大型集群计算机进行系统和全面的作业级资源使用度量,并将其集成到XDMoD中,XDMoD是一种用于资源管理的报告和分析框架,旨在满足用户、应用程序开发人员、系统管理员、系统管理人员和资金管理人员的信息需求。会计、调度器和事件日志与TACC_Stats的系统性能数据集成在一起。TACC_Stats定期记录资源使用情况,包括每个节点上运行的每个作业的许多硬件计数器。此外,通过节点(作业)级数据的聚合获得系统级度量。对这些数据的分析可以生成许多类型的标准和定制报告,甚至可以提供有限的预测能力,这在以前的开源、基于linux的软件系统中是不可用的。本文介绍了可用于有效资源管理的信息案例研究。我们相信这个系统是第一个完全全面的系统,用于支持基于开源软件的HPC系统中所有利益相关者的信息需求。
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Enabling comprehensive data-driven system management for large computational facilities
This paper presents a tool chain, based on the open source tool TACC_Stats, for systematic and comprehensive job level resource use measurement for large cluster computers, and its incorporation into XDMoD, a reporting and analytics framework for resource management that targets meeting the information needs of users, application developers, systems administrators, systems management and funding managers. Accounting, scheduler and event logs are integrated with system performance data from TACC_Stats. TACC_Stats periodically records resource use including many hardware counters for each job running on each node. Furthermore, system level metrics are obtained through aggregation of the node (job) level data. Analysis of this data generates many types of standard and custom reports and even a limited predictive capability that has not previously been available for open-source, Linux-based software systems. This paper presents case studies of information that can be applied for effective resource management. We believe this system to be the first fully comprehensive system for supporting the information needs of all stakeholders in open-source software based HPC systems.
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Distributed-memory parallel algorithms for generating massive scale-free networks using preferential attachment model Enabling comprehensive data-driven system management for large computational facilities There goes the neighborhood: Performance degradation due to nearby jobs A distributed dynamic load balancer for iterative applications Predicting application performance using supervised learning on communication features
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