权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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，需要计算堆栈的所有层共同努力进行能源优化。低层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，而高层解决方案则依赖于编程模型和应用软件设计的创新。不幸的是，几乎没有解决方案可以弥合两者之间的差距，导致错过了低层解决方案的机会，而高层解决方案的部署也受到限制。该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交汇的十字路口（即语言运行时）进行创新来简化高层和低层解决方案。该项目的智力价值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层应用程序信息和低层系统信息中提取。具体地说，该项目将解决两个基本问题，简化应用级能源管理与底层系统：（1）干扰：应用级能源管理如何在复杂的多线程和多处理的存在下保持有效，以及（2）不确定性：来自不同层的解决方案如何在每个层都可能引入自己的不确定性的环境中组合。该解决方案的亮点包括一个双向的能量调解器，以优雅地调解来自线程和进程的干扰，以及一个随机的能量优化器服务，以在存在不确定性的情况下持续优化能量消耗。首先，在托管语言运行时级别研究能量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，再到基于JavaScript的Web应用程序。第二，该项目的跨层面性质将通过讲习班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，并将导致计算前沿的课程丰富。该项目将产生许多算法，编译器实现，托管运行时实现，以及形成能量感知语言运行时生态系统的工具。该项目产生的所有软件工件都将是开源的，它们的存储库-连同所有实验数据和出版物-将http://www.cs.binghamton.edu/~davidl/GreenRuntime在整个项目期间在www.example.com上提供，该奖项反映了NSF的法定使命，并通过使用基金会的智力价值进行评估，被认为值得支持和更广泛的影响审查标准。