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，从计算堆栈的所有层中都需要进行一致的能源优化努力。低层解决方案为硬件和系统软件提供了创新的设计，以提高能效，而高层解决方案依赖于编程模型和应用程序软件设计的创新。不幸的是，很少有解决方案可以弥合两者之间的差距，从而导致缺少低层解决方案的机会和有限的更高层解决方案的部署。该项目的目的是通过在一个十字路口进行创新来简化高层和低层的解决方案，在该十字路口的应用程序和系统的物理世界相遇的逻辑世界中，即语言运行。具体而言，该项目将解决使用基础系统简化应用程序级别能源管理的两个基本问题：（1）干扰：在存在复杂的多线程和多处理的情况下，应用程序水平的能源管理如何保持有效，以及（2）不确定：来自不同层的解决方案如何在各个层中构成每层不在层面的环境中构成其自身份额的份额。解决方案的亮点包括双向能量介体，以优雅地介导线和过程中的干扰，以及在不确定性存在下连续优化能量消耗的随机能量优化器服务。项目的更广泛影响构成了几个维度。首先，在托管语言运行时研究能源优化有可能影响我们当今使用的大多数计算平台，从启用Android的智能手机到在数据中心部署到JavaScript-Path-Power Web Applications的图形处理引擎。其次，该项目的跨层性质将通过研讨会和研讨会使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，并将导致计算领域的课程丰富。该项目将产生许多算法，编译器实施，托管运行时实现和构成能源意识语言运行时生态系统的工具。该项目生产的所有软件工件将是开源的，它们的存储库以及所有实验数据和出版物将在http://www.cs.binghamton.edu/~davidl/~davidl/greenruntime project的整个过程中持续3年，并在项目中持续3年的统计数据，并在项目中均连续3年均可提供。值得通过基金会的智力优点和更广泛的影响审查标准来通过评估来支持。