Collaborative Research: ST-HEC: Scalable, Interoperable Tools to Support Autonomic Optimization of High-End Applications

合作研究：ST-HEC：支持高端应用自主优化的可扩展、可互操作工具

• 批准号: 0444207
• 负责人: Gary Tyson
• 金额: $ 19.02万
• 依托单位: Florida State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-11-01 至 2008-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0444207&HistoricalAwards=false
• 关键词: Collaborative; Research; ST; HEC; Scalable

Extremely large scale systems offer a new challenge to application designers. Current software development techniques do not scale well in execution efficiency on these systems or, more importantly, in the amount of time the programmer spends writing, debugging, and tuning the software. To realize extreme-scale computing, we must increase programmer productivity. To that end, we require three advances in the programming paradigm for these systems. First, the application programmer must interact with the development environment at a level higher than processesor execution threads. Tools must support these interaction modes and more abstract application views. Second, system monitoring functions must exist to provide feedback to the application programmer on overall system performance. Monitoring an extreme-scale system must include some degree of automation and must be able to infer overall performance from a small set of monitoring points. Feedback must be compressed to highlight performance issues at the high abstraction level the programmer requires. Finally, many low-level optimization decisions must be automated by incorporating a new generation of compiler optimizations targeting global program behavior, andthese must be intimately integrated with the monitoring system. These advances are described collectively as autonomic performance optimization.Our proposed research addresses these requirements by developing new tools and extending current tools to manage large software projects. We will extend our prior work on the Tau framework of performance instrumentation and analysis tools to the scale used by these HEC applications. To do this, we will incorporate a new framework for monitoring representative "skeletons" that can provide information to the programmer about total system performance by using a simpler model that matches the execution profile of the full application. Performance modeling of the skeleton is achieved by placement of profile monitors at strategic points in the system. We will utilize advancedmachine learning techniques to determine the placement of these monitor points, as well as to synthesize the resulting large quantity of performance information into the proper form for the application designer. Finally, we will automate some critical low-level design decisions by feeding profile data directly to the compiler and dynamic code translator. The optimizations developed target data layout, data duplication throughout the system, and dynamic data movement. Optimizing data management will decrease average access latency for memory references, reducing congestion on the inter-processor and inter-cluster networks while freeing the programmer from making detailed data placement decisions.The intellectual merit of this proposal is in the new paradigm of autonomic performance optimization as a framework for the integration of performance methods and tools for HEC systems. The broader impact is both technical and societal. Ultimately, we strive to enhance the computational tool infrastructure used to solve Grand Challenge scientific computing problems. However, we believe this must be done in association with the evolution of large-scale computing to use introspective, autonomic platforms and systems. Our work will enable practitioners to more easily build efficient, scalable applications, to solve very large and complex problems, and to do so more quicklythan is currently feasible. The significant increase in the productivity of applications writers will not only enhance the development of scientific applications important to our national infrastructure, but will also open HEC to important economic and societal applications where computing is advancing science and technology.

超大规模系统给应用程序设计人员带来了新的挑战。当前的软件开发技术在这些系统上的执行效率方面没有很好地扩展，或者更重要的是，在程序员花费在编写、调试和调优软件上的时间量方面没有很好地扩展。为了实现极限规模计算，我们必须提高程序员的生产力。为此，我们需要在这些系统的编程范式中取得三项进展。首先，应用程序编程人员必须在比进程或执行线程更高的级别上与开发环境交互。工具必须支持这些交互模式和更抽象的应用程序视图。第二，系统监控功能必须存在，以便向应用程序程序员提供关于整个系统性能的反馈。监控极端规模的系统必须包括一定程度的自动化，并且必须能够从一小组监控点推断出整体性能。反馈必须被压缩，以突出程序员所需的高抽象级别的性能问题。最后，许多低级别的优化决策必须通过结合新一代针对全局程序行为的编译器优化来自动化，并且这些优化必须与监控系统紧密集成。这些进步被统称为自主性能优化。我们提出的研究通过开发新的工具和扩展现有的工具来管理大型软件项目，以满足这些需求。我们将扩展我们以前的工作的性能仪表和分析工具的Tau框架，这些HEC应用程序所使用的规模。要做到这一点，我们将纳入一个新的框架，用于监控代表性的“骨架”，可以提供信息给程序员的总系统性能，通过使用一个更简单的模型，匹配完整的应用程序的执行配置文件。骨架的性能建模是通过在系统中的战略点放置配置文件监视器来实现的。我们将利用先进的机器学习技术来确定这些监控点的位置，并将由此产生的大量性能信息合成为应用程序设计人员的适当形式。最后，我们将通过直接向编译器和动态代码翻译器提供概要文件数据来自动化一些关键的低级设计决策。优化开发了目标数据布局、整个系统的数据复制和动态数据移动。优化数据管理将减少内存引用的平均访问延迟，减少处理器间和集群间网络上的拥塞，同时使程序员从详细的数据布局decisions.The智力的优点，这个建议是在自主性能优化的新范式作为一个框架，为HEC系统的性能方法和工具的集成。更广泛的影响是技术和社会的。最终，我们努力增强用于解决大挑战科学计算问题的计算工具基础设施。然而，我们认为这必须与大规模计算的发展相关联，以使用内省的自主平台和系统。我们的工作将使从业者能够更容易地构建高效，可扩展的应用程序，以解决非常大和复杂的问题，并且比目前可行的更快。应用程序作者的生产力的显着提高不仅将加强对我们国家基础设施重要的科学应用的开发，而且还将使HEC向重要的经济和社会应用开放，其中计算正在推进科学和技术。