HPC Software Tracking Strategies for a Diverse Workload

Q4 Social Sciences Meta: Avaliacao Pub Date : 2020-11-01 DOI:10.1109/HUSTProtools51951.2020.00008
Heechang Na, Zhi-Qiang You, Troy Baer, Shameema Oottikkal, Trey Dockendorf, Scott Brozell
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引用次数: 1

Abstract

This paper discusses and reviews various tools that Ohio Supercomputer Center (OSC) uses for tracking High-Performance Computing (HPC) software usage. Tracking software usage is a key element to support a diverse user community. OSC maintains three data pools: batch job database, data from user executions, and license server logs. We utilize various tools and databases to collect, store and analyze software usage data. These includes pbsacct, Spark, XALT, Lmod, Splunk, License log, Ganglia, and Prometheus/Grafana. We discuss the coverage and gaps for each pool and present some use cases.
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不同工作负载下的高性能计算软件跟踪策略
本文讨论并回顾了俄亥俄超级计算机中心(OSC)用于跟踪高性能计算(HPC)软件使用情况的各种工具。跟踪软件使用情况是支持多样化用户社区的关键因素。OSC维护三个数据池:批处理作业数据库、来自用户执行的数据和许可证服务器日志。我们利用各种工具和数据库来收集、存储和分析软件使用数据。其中包括pbsacct、Spark、XALT、Lmod、Splunk、License log、Ganglia和Prometheus/Grafana。我们讨论每个池的覆盖范围和缺口,并给出一些用例。
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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来源期刊
Meta: Avaliacao
Meta: Avaliacao Social Sciences-Education
CiteScore
0.40
自引率
0.00%
发文量
13
审稿时长
10 weeks
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