SHF: Small: Green Parallel Language Systems

SHF：小型：绿色并行语言系统

• 批准号: 1526205
• 负责人: Yu David Liu
• 金额: $ 48.45万
• 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: 2015
• 资助国家: 美国
• 起止时间: 2015-07-01 至 2019-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1526205&HistoricalAwards=false
• 关键词: SHF; Small; Green; Parallel; Language

Title: SHF:Small:Green Parallel Language SystemsThe broad spectrum of modern parallel computing platforms are faced with the growing concern about energy consumption. This project explores the frontier at the converging point of parallel computing and energy-efficient computing. The intellectual merits of the proposal are to study energy efficiency of parallel computing through novel programming language and compiler technologies, and produce a set of energy-optimizing compilers, language runtimes, and programming models for parallel systems. With parallel platforms pervasively deployed in the modern computing world, the project's broader significance and importance are as diverse as reducing the operational cost of data centers, improving system reliability of mission-critical systems, extending the battery life of handheld devices, and promoting the environmental sustainability of our society. The unifying theme of the project is to exploit program structures and run-time semantic information for optimizing energy consumption of multi-threaded programs on parallel architectures. Concretely, the project consists of three interconnected directions. (i) energy-efficient thread management, that is, optimizing energy consumption through coordinating threads based on their static and dynamic dependencies. (ii) energy-efficient data management, that is, promoting energy proportionality through analyzing data-intensive programs in a setting where data rates, data processing schedules, and CPU frequencies may interplay in complex fashions. (iii) energy-efficient synchronization management, that is, harvesting different thread synchronization patterns to achieve pattern-specific energy optimization. Together, the three directions constitute a unique and important dimension of energy optimization for parallel computing platforms.

职务名称：SHF：Small：绿色并行语言系统现代并行计算平台的广泛应用都面临着日益增长的能耗问题。这个项目探索了并行计算和节能计算的融合点。该提案的智力价值是通过新颖的编程语言和编译器技术研究并行计算的能源效率，并产生一组用于并行系统的能源优化编译器，语言运行时和编程模型。随着并行平台在现代计算世界中的广泛部署，该项目的更广泛意义和重要性与降低数据中心的运营成本，提高关键任务系统的系统可靠性，延长手持设备的电池寿命以及促进我们社会的环境可持续性一样多。该项目的统一主题是利用程序结构和运行时语义信息优化并行架构上多线程程序的能耗。具体而言，该项目由三个相互关联的方向组成。(i)节能线程管理，即通过基于线程的静态和动态依赖性协调线程来优化能耗。(ii)高能效数据管理，即通过分析数据密集型程序，在数据速率、数据处理时间表和CPU频率可能以复杂的方式相互作用的环境中，提高能源比例。(iii)节能同步管理，即收获不同的线程同步模式以实现特定于模式的能量优化。这三个方向共同构成了并行计算平台能量优化的独特而重要的维度。