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

今天的许多性能关键型应用程序涉及操纵数据集，这些数据集要么太大，要么很少使用，以至于无法在本地内存缓存或本地缓存服务器中可靠地找到。 由于处理器周期与磁盘和网络访问延迟之间的巨大差异，这些应用程序浪费了很大一部分时间等待数据;展望未来，这个问题预计会变得更糟。 为了克服这个问题，本研究将联合收割机结合主动存储预取和智能缓存管理，充分隐藏数据访问延迟，同时智能地使用内存资源。 将开发程序转换工具和运行时支持系统，它们将协作为数据密集型应用定制内存层次结构和分布式缓存资源管理。 通过对内存、网络和磁盘资源的应用程序特定指导，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，也让程序员永远不会浪费宝贵的编程时间来考虑这个性能问题。

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.