CSR :Small: Exploiting Slowdowns for Speedup in Power-Scalable HPC Systems.

先进的计算系统-支持经济、科学和医学等领域的各种应用-越来越多地在设计时考虑到能源效率。一种现有的能源管理方法是以不同的电压和频率运行底层处理器和设备。通常，这些方法会推动设备在热量限制内尽可能快地运行，前提是“越快越好，或者至少不会造成损害。”在流行的文献中，有越来越多的证据表明“越慢有时越好”。例如，对于IOZone这样的基准应用程序，已经观察到以更快的速度运行处理器可能会导致总体执行时间显著减慢。在大规模情况下，例如在Amazon Web Services云中，这种性能损失可能会花费数十万美元的CPU时间，并浪费通常由污染化石燃料产生的宝贵能源。然而，在当今数据中心规模的复杂系统中找出这种速度放缓的根本原因，就像大海捞针一样。现在，性能是应用程序设计、系统资源和底层硬件之间复杂交互的函数。此外，功率扩展使硬件的原始性能成为一个变量；因此，进一步混淆了隔离减速的尝试。该项目构建了新的技术，当启用功率扩展时，该技术可以识别、建模并自动最小化或消除并行和分布式应用中的减速。关键方法是细粒度应用程序和内核插装，以开发对并行和分布式应用程序之间的交互以及软件和硬件堆栈的深入分析。这项研究的智力价值涉及三个中间研究目标：1)对大类应用程序和不同系统集进行详尽的测试和深入的系统和代码分析，以分类和隔离由于功率扩展而导致的速度减慢现象；2)设计、实现和验证对速度减慢敏感的应用程序的关键路径的模型；以及3)分析结果模型，以及设计、实现和验证自动化的、开源的运行时优化技术，以控制功率扩展以最小化或消除速度减慢。该项目的完成将提高高级系统的性能和能效。采用生成的运行时工具将支持使用功率扩展来节省能源，同时减少现代应用程序和系统的解决方案时间。由此产生的艺术品和技术将通过解决在电力限制下构建大规模系统的挑战来提高美国的竞争力。教育活动将有助于培养出具有高度市场技能的多样化毕业生。将开源并公开的研究发现和软件工具整合到教育课程中，将有助于吸引下一代计算机科学家的兴趣。

Advanced computing systems --- that support a wide variety of applications in fields such as economics, sciences, and medicine --- are increasingly being designed with energy efficiency considerations. An extant approach to energy management is to run the underlying processors and devices at varying voltage and frequency. Typically, the approaches push the devices to run as fast as possible within thermal limits using the premise that "faster is better or at least does no harm." There is growing evidence in the prevailing literature that "slower is sometimes better." For example, for benchmark applications such as IOZone, it has been observed that running the processors at a faster speed can lead to significant slowdowns in the overall execution time. At large scale, e.g., in the Amazon Web Services cloud, such performance loss can cost hundreds of thousands of dollars in CPU hours and waste precious energy often begotten from polluting fossil fuels. However, isolating the root cause of such slowdowns in today's complex systems at the scale of data centers is akin to finding a needle in a haystack. Performance is now a function of the complex interaction between application design, system resources, and the underlying hardware. Furthermore, power scaling makes the raw performance of the hardware a variable; thus, further confounding attempts to isolate slowdowns.This project builds novel technologies that identify, model and automate the minimization or elimination of slowdowns in parallel and distributed applications when power scaling is enabled. The key approach is fine-grain application and kernel instrumentation to develop in-depth analysis of the interaction between parallel and distributed applications and the software and hardware stack. The intellectual merit of this research involves three intermediate research goals: 1) Exhaustive testing and deep system and code analysis on a large class of applications and a diverse set of systems to classify and isolate the slowdown phenomenon due to power scaling; 2) Design, implementation, and validation of models of the critical paths of applications exhibiting sensitivity to slowdowns; and 3) Analysis of the resulting models and design, implementation, and validation of the automated, open-source, runtime optimization techniques to steer power scaling to minimize or eliminate slowdowns.Completion of the project will improve the performance and energy efficiency of advanced systems. Adoption of the resulting runtime tools will enable use of power scaling to save energy while simultaneously reducing time-to-solution for modern applications and systems. The resulting artifacts and technologies will contribute to U.S. competitiveness by addressing the challenge of building large-scale systems within power constraints. The educational activities will help produce diverse graduates with highly marketable skill sets. The integration of the research discoveries and software tools, which will be open source and made public, into the educational curriculum will help capture the interest of the next generation of computer scientists.