Software: Improving Performance of Out-Of-Core Applications with Memory Access Pattern Guided Prefetching, Page Replacement and Clustering

软件：通过内存访问模式引导的预取、页面替换和集群来提高核外应用程序的性能

• 批准号: 0232647
• 负责人: Yiming Hu
• 金额: $ 5.22万
• 依托单位: University of Cincinnati Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-01-15 至 2004-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0232647&HistoricalAwards=false
• 关键词: Software; Improving; Performance; Out; Core

Many large, important scientific and commercial applications use very large data sets that do not fit into available RAM. Such applications have to frequently move portions of their data sets between main memory and secondary storage (such as disks). Unfortunately, because disks are several orders of magnitude slower than RAM, many large applications spend most of their running time on disk I/Os, resulting in a significant performance degradation.A lot of research has been carried out to address this problem. Generally, the previous solutions include application-specific algorithms and libraries, compiler-based techniques, and limited Operating System (OS) support. While these solutions are effective, they also have some serious drawbacks. In this proposal, the PI proposes careful OS and architecture co-designs of several novel methods that can significantly improve the performance of large, memory-intensiveapplications. The basic idea is to use hardware/software to collect and analyze the runtime memory access patterns of applications. The knowledge of these patterns is then used to guide prefetching and to improve the page-replacement algorithms.Preliminary results from the proposed systems show very promising improvements in performance. They can drastically reduce the amount ofpage I/O traffic, and hide latencies of many page I/Os. Simulation results based on NPB (NAS Parallel Benchmarks) benchmarks show that pattern-guided prefetching and page-replacement can improve performance by up to 100 times. The proposed methods are transparent to user applications. The proposed studies, if successful, will have broad impacts on the field of high performance computing.

许多大型，重要的科学和商业应用都使用了不适合可用RAM的非常大的数据集。这些应用程序必须经常在主内存和辅助存储（例如磁盘）之间移动其数据集的部分。不幸的是，由于磁盘比RAM慢几个数量级，因此许多大型应用程序将大部分运行时间都花在磁盘I/OS上，从而导致了大量的性能降级。已经进行了许多研究来解决此问题。通常，以前的解决方案包括特定于应用程序的算法和库，基于编译器的技术以及有限的操作系统（OS）支持。尽管这些解决方案有效，但它们也有一些严重的缺点。在此提案中，PI提出了几种新型方法的仔细的OS和体系结构共同设计，这些方法可以显着改善大型内存 - 封闭式倾斜的性能。基本思想是使用硬件/软件来收集和分析应用程序的运行时内存访问模式。然后，这些模式的知识被用来指导预摘要并改善拟议系统的重复算法结果。拟议系统的预定结果显示出非常有希望的性能改善。它们可以大大减少页面I/O流量的数量，并隐藏许多页I/O的潜伏期。基于NPB（NAS并行基准）基准测试的仿真结果表明，模式引导的预取the和页面替换可以提高性能高达100倍。提出的方法对用户应用程序透明。拟议的研究（如果成功）将对高性能计算领域产生广泛的影响。