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CNS Core: Small: Language Runtime Support for Energy-Aware Applications

CNS 核心：小型：对能源感知应用程序的语言运行时支持

基本信息

批准号：
1910532
负责人：
Yu David Liu
金额：
$ 47.86万
依托单位：
SUNY at Binghamton
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1910532&HistoricalAwards=false
关键词：
CNS Core Small Language Runtime

项目摘要

As energy consumption becomes a unifying concern for modern computing platforms, concerted efforts on energy optimization are required from all layers of the computing stack. Lower-layer solutions offer innovative designs on hardware and systems software to improve energy efficiency, whereas higher-layer solutions rely on innovations over programming models and application software design. Unfortunately, few solutions exist to bridge the gap in between, leading to missed opportunities for lower-layer solutions and limited deployment of higher-layer solutions. The goal of this project is to streamline the higher-layer and lower-layer solutions through innovations at a crossroads where the logical world of the application and the physical world of the systems meet, namely, language runtimes.The intellectual merit of the project lies in a number of runtime-centric cross-layer energy optimization solutions that judiciously draw from both higher-level application information and lower-level system information. Concretely, this project will address two fundamental problems in streamlining application-level energy management with underlying systems: (1) interference: how application-level energy management can remain effective in the presence of complex multi-threading and multi-processing, and (2) uncertainty: how solutions from different layers can compose in an environment where each layer may introduce its own share of uncertainty. The highlights of the solutions include a bi-directional energy mediator to gracefully mediate the interference from threads and processes, and a stochastic energy optimizer service to continuously optimize energy consumption in the presence of uncertainty.The broader impacts of the project constitute several dimensions. First, studying energy optimization at the level of managed language runtimes has the potential to impact a majority of computing platforms we use today, ranging from Android-enabled smartphones, to graph processing engines deployed on data centers, to Javascript-powered web applications. Second, the cross-layer nature of this project will bring researchers from different communities together, through workshops and seminars. Third, the project presents research opportunities for underrepresented students, and will lead to curriculum enrichment at the frontier of computing.The project will yield a number of algorithms, compiler implementations, managed runtime implementations, and tools that form the ecosystem of an energy-aware language runtime. All software artifacts produced from the project will be open-source, and their repositories - together with all experimental data and publications - will be made available at http://www.cs.binghamton.edu/~davidl/GreenRuntime throughout the duration of the project, and continuously maintained for an additional 3 years after the completion of the project.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

随着能源消耗成为现代计算平台的统一关注点，计算堆栈的所有层都需要协同努力进行能源优化。较低层次的解决方案提供硬件和系统软件的创新设计，以提高能源效率，而较高层的解决方案依赖于编程模型和应用软件设计的创新。不幸的是，很少有解决方案能够弥合两者之间的差距，从而导致错失了低层解决方案的机会，并限制了高层解决方案的部署。这个项目的目标是通过在应用程序的逻辑世界和系统的物理世界相遇的十字路口（即语言运行时）进行创新来简化高层和低层解决方案。该项目的智力优势在于许多以运行时为中心的跨层能量优化解决方案，这些解决方案明智地从高级应用程序信息和低级系统信息中提取信息。具体而言，该项目将解决两个基本问题，即通过底层系统简化应用级能源管理：(1)干扰：应用级能源管理如何在复杂的多线程和多处理存在的情况下保持有效；(2)不确定性：来自不同层的解决方案如何在每个层可能引入自己的不确定性的环境中组成。该解决方案的亮点包括一个双向能量中介器，用于优雅地调解线程和进程的干扰，以及一个随机能量优化器服务，用于在存在不确定性的情况下持续优化能耗。该项目的更广泛影响包括几个方面。首先，在托管语言运行时级别上研究能源优化有可能影响我们今天使用的大多数计算平台，从支持android的智能手机，到部署在数据中心的图形处理引擎，再到javascript驱动的web应用程序。其次，这个项目的跨层性质将通过研讨会和研讨会将来自不同社区的研究人员聚集在一起。第三，该项目为代表性不足的学生提供了研究机会，并将导致计算机前沿课程的丰富。该项目将产生许多算法、编译器实现、托管运行时实现和工具，这些构成了能源感知语言运行时的生态系统。项目产生的所有软件构件都将是开源的，它们的存储库——连同所有实验数据和出版物——将在项目期间在http://www.cs.binghamton.edu/~davidl/GreenRuntime上提供，并在项目完成后继续维护3年。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。