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

超大规模系统对应用程序设计人员提出了新的挑战。当前的软件开发技术在这些系统上不能很好地扩展执行效率，或者更重要的是，不能很好地扩展程序员编写、调试和调优软件所花费的时间。为了实现极端规模的计算，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方面取得三个进展。首先，应用程序程序员必须在高于处理器或执行线程的级别上与开发环境进行交互。工具必须支持这些交互模式和更抽象的应用程序视图。其次，必须存在系统监视功能，以便向应用程序程序员提供有关整体系统性能的反馈。监视一个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整体性能。反馈必须被压缩，以突出程序员需要的高抽象级别的性能问题。最后，许多低级优化决策必须通过结合针对全局程序行为的新一代编译器优化来实现自动化，并且这些优化必须与监视系统紧密集成。这些进步统称为自主性能优化。我们建议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来解决这些需求。我们将把我们之前在Tau框架的性能仪器和分析工具上的工作扩展到这些HEC应用程序所使用的规模。要做到这一点，我们将合并一个新的框架，用于监视具有代表性的“骨架”，这些骨架可以通过使用与整个应用程序的执行概要相匹配的更简单的模型，向程序员提供有关总体系统性能的信息。骨架的性能建模是通过在系统中的战略点放置概要监视器来实现的。我们将利用先进的机器学习技术来确定这些监测点的位置，并将由此产生的大量性能信息综合为应用程序设计人员的适当形式。最后，我们将通过将概要文件数据直接提供给编译器和动态代码转换器来实现一些关键的低级设计决策的自动化。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的拥塞，同时将程序员从制定详细的数据放置决策中解放出来。该建议的智力价值在于自主性能优化的新范式，作为HEC系统性能方法和工具集成的框架。更广泛的影响包括技术和社会两方面。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结合，以使用内省、自主的平台和系统。我们的工作将使从业者能够更容易地构建高效、可伸缩的应用程序，解决非常大而复杂的问题，并且比目前可行的更快地完成这些工作。应用程序编写者生产力的显著提高不仅将促进对我们国家基础设施至关重要的科学应用程序的发展，而且还将使HEC向重要的经济和社会应用程序开放，其中计算正在推动科学和技术的发展。

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.