Trace-Driven Evaluations of the Memory Behavior of Large Commercial Applications

大型商业应用内存行为的跟踪驱动评估

• 批准号: 0105761
• 负责人: Michel Dubois
• 金额: $ 28.06万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-09-01 至 2005-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0105761&HistoricalAwards=false
• 关键词: Trace; Driven; Evaluations; Memory; Behavior

Computer architecture research is based on experimental evaluation of application behavior.One critical issue in the architecture of high-end enterprise servers is the design of the memoryhierarchy, which must be designed to support current and future data-intensive applications.Few evaluations of the memory behavior of large commercial applications exist, especially in thepublic domain. The research proposed here is based on a collaboration between IBM and USC totake advantage of the IBM Watson Server Performance Laboratory to explore the memorybehavior of high-end commercial applications.More specifically, it is proposed to use the IBM MemorIES board to collect bus activitytraces from a modern server machine running large, finely tuned OLTP, DSS and Web workloads.Because of the sheer size of the traces, samples of transaction records will be collected only in selected time intervals.Besides obtaining traces, the goal of this project is to characterize the memorybehavior of these applications, to evaluate alternative memory hierarchies for futurehigh-end commercial servers, and to explore new multiprocessor architectures forcommercial systems. The experimental environment provided by the IBM Watson Server Performance Laboratorycost millions of dollars and years of effort to setup. This proposed research will leverage theseefforts. It is a unique opportunity to collect and disseminate traces and experimental datawhich would be practically impossible to obtain under any reasonable research budget.

计算机体系结构研究基于对应用程序行为的实验评估。高端企业服务器体系结构中的一个关键问题是内存层次结构的设计，其设计必须能够支持当前和未来的数据密集型应用程序。对大型商业应用程序的内存行为的评估很少，尤其是在公共领域。这里提出的研究基于 IBM 和 USC 之间的合作，利用 IBM Watson 服务器性能实验室来探索高端商业应用程序的内存行为。更具体地说，建议使用 IBM MemorIES 板从运行大型、精细调整的 OLTP、DSS 和 Web 工作负载的现代服务器收集总线活动跟踪。由于跟踪的规模巨大，事务记录的样本将被收集到。 仅在选定的时间间隔内收集。除了获取跟踪之外，该项目的目标是表征这些应用程序的内存行为，评估未来高端商业服务器的替代内存层次结构，并探索商业系统的新多处理器架构。 IBM Watson 服务器性能实验室提供的实验环境花费了数百万美元和多年的努力来设置。这项拟议的研究将利用这些努力。这是收集和传播痕迹和实验数据的独特机会，而在任何合理的研究预算下几乎不可能获得这些数据。