SHF: Small: Scalable Trace-Based Tools for In-Situ Data Analysis of HPC Applications (ScalaJack)

SHF：小型：用于 HPC 应用程序现场数据分析的可扩展的基于跟踪的工具 (ScalaJack)

• 批准号: 1217748
• 负责人: Frank Mueller
• 金额: $ 45.74万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-06-01 至 2017-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1217748&HistoricalAwards=false
• 关键词: SHF; Small; Scalable; Trace; Based

Production codes on supercomputers are struggling to remain scalableeach time the processor core count increases by a factor of 10, eventhough they run efficiently at smaller scale.But root cause diagnosis fails at petascale since (1) symptoms ofperformance problems can be subtle, (2) only fewmetrics can be efficiently collected and (3) tools can only feasibly recorda small subset of even these metrics.This work addresses these problems by creating a framework that allowsapplication developers to focus on data analysis that drives customizeddata extraction combined with on-the-fly analysis specifically gearedto their individual problems. This is accomplished by combining traceanalysis and in-situ data analysis techniques at runtime, therebylifting data reduction to a new level where it IS analysis. With thisapproach, modular measurement and analysis components are combined toselectively extract representative data from production codes in aproblem-specific manner, which enables root cause analysis.The work demonstrates the feasibility of customized dataextraction and analysis at scale for root cause analysis on currentand forthcoming multi-petascale supercomputers. It thus contributesto sustain scalable scientific computing into the future up to the largestscales. Results of this work will be contributed as open-source codeto the research community and beyond as done, allowing other groups tonot only build tools on top of our framework but also contribute theirown components.

超级计算机上的生产代码很难保持可伸缩性-每次处理器核心计数增加10倍，即使它们在较小的规模下高效运行。但根本原因诊断在千万亿级失败，因为(1)性能问题的症状可能很微妙，(2)只能有效地收集几个指标，(3)工具只能可行地记录这些指标的一小部分。这项工作通过创建一个框架来解决这些问题，该框架允许应用程序开发人员专注于数据分析，以驱动定制的数据提取与特别针对他们的个别问题的动态分析。这是通过在运行时结合跟踪分析和现场数据分析技术来实现的，从而将数据简化提升到一个新的水平，在那里进行分析。该方法将模块化测量和分析组件结合起来，以特定问题的方式从生产代码中选择性地提取代表性数据，从而实现根本原因分析。该工作论证了定制数据提取和规模分析在当前和即将到来的数百亿级超级计算机上进行根本原因分析的可行性。因此，它有助于将可伸缩的科学计算支持到未来的最大规模。这项工作的结果将以开源代码的形式提供给研究社区，并以此为基础，允许其他团队不仅在我们的框架上构建工具，还可以贡献他们的开发组件。