ITR: Static and Dynamic Techniques for Latency Hiding in Data-Intensive Applications

ITR：数据密集型应用程序中延迟隐藏的静态和动态技术

• 批准号: 0085938
• 负责人: Todd Mowry
• 金额: $ 80万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-09-01 至 2004-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0085938&HistoricalAwards=false
• 关键词: ITR; Static; Dynamic; Techniques; Latency

Many of today's performance-critical applications involve manipulating datasets that are either too large or too rarely used to be reliably found inlocal memory caches or local cache servers. Due to the enormous disparitybetween processor cycle times and disk and network access latencies, theseapplications waste a large fraction of their time waiting for data; as welook to the future, this problem is expected to become even worse. Toovercome this problem, this research will combine aggressive storageprefetching with intelligent cache management to fully hide the data accesslatency while using memory resources intelligently. Program transformationtools and runtime support systems will be developed that collaborate tocustomize memory hierarchy and distributed cache resource management fordata-intensive applications. With application-specific guidance of memory,network and disk resources, it is possible to decrease execution times byorders of magnitude. The ultimate goal of the research is for programs tonever waste time waiting for data, and for programmers to never waste theirvaluable programming time thinking about this performance problem.

当今的许多性能关键型应用程序都涉及操作数据集，这些数据集要么太大，要么很少使用，无法在本地内存缓存或本地缓存服务器中可靠地找到。 由于处理器周期时间与磁盘和网络访问延迟之间存在巨大差异，这些应用程序浪费了大量时间来等待数据；展望未来，这个问题预计会变得更加严重。 为了克服这个问题，本研究将主动存储预取与智能缓存管理相结合，在智能使用内存资源的同时完全隐藏数据访问延迟。 将开发程序转换工具和运行时支持系统，以协作为数据密集型应用程序定制内存层次结构和分布式缓存资源管理。 通过内存、网络和磁盘资源的特定于应用程序的指导，可以将执行时间减少几个数量级。 这项研究的最终目标是让程序永远不会浪费时间等待数据，并且让程序员永远不会浪费宝贵的编程时间来思考这个性能问题。