CPA-CPL: Feedback-Directed Resource Management in Virtual Private Machines

CPA-CPL：虚拟专用机中反馈导向的资源管理

• 批准号: 0811427
• 负责人: Steven Carr
• 金额: $ 6万
• 依托单位: Michigan Technological University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2010-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0811427&HistoricalAwards=false
• 关键词: CPA; CPL; Feedback; Directed; Resource

In modern computers with multiple CPUs per chip, multiple levels ofcomputer memory are shared among the CPUs. This sharing may causevarying response times for computer applications, potentiallyaffecting their usability. For instance, a video streamingapplication that has unpredictable response times due to interferencefrom other applications may be essentially unusable. Thus, thecomputer system must attempt to allocate shared memory resourcesequitably to increase response consistency. Virtual Private Machines(VPMs), in which shared computer resources are partitioned to givethe illusion that a program is running on a single physical CPUrunning in isolation, are one method to provide responseconsistency. This research involves the development of softwaremodeling techniques to predict the memory requirements of applicationsrun on modern computers and using those predictions to guide memoryresource allocation to provide consistent, predictable performance.The focus of this research consists of the development ofreuse-distance-based memory locality analysis for multi-threadedapplications to predict the resource requirements of thoseapplications. This research will apply the new memory locality modelsto manage the cache and bandwidth requirements for multi-threadedapplications run on a VPM within a multi-programmedenvironment. Specifically, this research will examine reuse distancefor Partitioned Global Address Space (PGAS) applications written inUnified Parallel C (UPC). The research will investigate the effects ofthe number of threads used per application on per-thread data size andthe effect on reuse distance of shared data caused by threadinteraction. The result will be novel solutions to memory localityanalysis for multi-threaded applications while allowing the compilerto predict and specify the resources needed to give multi-threadedapplications targeted performance.

在每个芯片有多个cpu的现代计算机中，多个级别的计算机内存在cpu之间共享。这种共享可能会导致计算机应用程序的响应时间不同，潜在地影响它们的可用性。例如，由于其他应用程序的干扰，具有不可预测响应时间的视频流应用程序可能本质上是不可用的。因此，计算机系统必须尝试公平地分配共享内存资源，以提高响应一致性。虚拟专用机（VPMs）是提供响应一致性的一种方法，其中对共享的计算机资源进行分区，使程序在单独运行的单个物理cpu上运行。这项研究涉及软件建模技术的发展，以预测在现代计算机上运行的应用程序的内存需求，并使用这些预测来指导内存资源分配，以提供一致的、可预测的性能。本研究的重点是开发基于重用距离的多线程应用程序内存局部性分析，以预测这些应用程序的资源需求。本研究将应用新的内存局部性模型来管理在多编程环境中的VPM上运行的多线程应用程序的缓存和带宽需求。具体来说，本研究将检查用统一并行C （UPC）编写的分区全局地址空间（PGAS）应用程序的重用距离。该研究将调查每个应用程序使用的线程数对每个线程数据大小的影响，以及线程交互对共享数据重用距离的影响。结果将为多线程应用程序提供新的内存位置分析解决方案，同时允许编译器预测和指定为多线程应用程序提供目标性能所需的资源。