Collaborative Research: CSR(SMA): Scalable performance modeling and analysis framework

合作研究：CSR(SMA)：可扩展的性能建模和分析框架

• 批准号: 0509133
• 负责人: Kirk Cameron
• 金额: $ 2.5万
• 依托单位: University South Carolina Research Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-01 至 2006-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0509133&HistoricalAwards=false
• 关键词: Collaborative; Research; CSR; SMA; Scalable

The project will explore novel software technologies to improve the time to solution of applications by increasing memory efficiency on emerging distributed systems. The approaches to be pursued include combination of simulation and hardware assists. The memory performance of distributed simulations must improve if the benefits of emergent high-end systems are to be realized. To hide the effects of memory latency, the complexity of systems used as building blocks (e.g. SMPs) in large-scale clusters has increased substantially. The combination of memory speed, node complexity and system scale often leads to poor performance. Years of intense research aimed at improving the performance of applications in parallel and distributed systems have led to average efficiencies of 5-10%. The continued exponential increase in complexity makes maintaining these efficiencies through tuning challenging. Improving efficiency dramatically will require innovation. The project will improve distributed simulation performance in SMP-based clusters through creation of a new set of software tools that enable access to coordinated hardware counter information across system components (e.g. CPU and NIC). Preliminary results indicate these multi-component techniques are inherently scalable while providing system-wide memory performance information previously unavailable without the aid of sophisticated, intrusive software profiling or simulation.The project will leverage hardware profiling, parallel and distributed performance evaluation, statistical data reduction analysis, analytical modeling techniques and tool development with emerging hardware counter technologies on commodity CPUs and NICs to produce a software framework for locality-aware application profiling, analysis and optimization, and create a framework that provides a complete picture of local and remote memory accesses in a largescale, high-end distributed systems .

该项目将探索新的软件技术，通过提高新兴分布式系统的内存效率来缩短解决应用程序的时间。所采用的方法包括模拟和硬件辅助相结合。如果要实现新兴高端系统的好处，分布式仿真的内存性能必须提高。为了隐藏内存延迟的影响，在大规模集群中用作构建块（例如SMP）的系统的复杂性大幅增加。内存速度、节点复杂性和系统规模的组合通常会导致性能低下。多年来致力于提高并行和分布式系统中应用程序性能的深入研究已经使平均效率达到5- 10%。复杂性的持续指数级增长使得通过调优来保持这些效率变得具有挑战性。大幅提高效率需要创新。该项目将通过创建一组新的软件工具来提高基于SMP的集群中的分布式仿真性能，这些软件工具可以访问跨系统组件（例如CPU和NIC）的协调硬件计数器信息。 初步结果表明，这些多组件技术具有内在的可扩展性，同时提供了以前在没有复杂的侵入性软件分析或模拟的帮助下无法获得的系统范围的内存性能信息。该项目将利用硬件分析，并行和分布式性能评估，统计数据减少分析，分析建模技术和工具开发，在商用CPU和CPU上使用新兴的硬件计数器技术，以产生用于本地感知应用程序分析的软件框架，分析和优化，并创建一个框架，提供了一个完整的图片，本地和远程内存访问的大规模，高端分布式系统。