AF: Small: Collaborative Research: Algorithmic Approaches to Energy-Efficient Computing

AF：小型：协作研究：节能计算的算法方法

• 批准号: 1217314
• 负责人: Marek Chrobak
• 金额: $ 17.1万
• 依托单位: University of California-Riverside
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-01 至 2016-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1217314&HistoricalAwards=false
• 关键词: AF; Small; Collaborative; Research; Algorithmic

Energy consumption is now emerging as a dominant performance measure in computer systems. In recent years, significant progress in improving energy efficiency has been accomplished by a combination of better hardware design and software tools. Yet the design of future energy-efficient computer systems will ultimately require the development of fundamental models and algorithmic tools that can be used to guide practical solutions.This project is to study algorithmic methods for improving energy efficiency of data processing and storage in computer systems. The basic approach is to model the operation of various system components in the language of combinatorial optimization, with the objective function representing energy consumption, and to solve these problems using exact or approximate efficient algorithms. Many of those problems can be formulated in terms of task scheduling, where the objective is to optimize the CPU energy consumption required to complete a collection of tasks, while meeting some performance requirements. Other examples include minimizing energy consumption of memory systems, both the internal and external memories, by optimizing power levels and sophisticated paging or caching strategies. In addition to addressing some specific energy optimization problems, this work is expected to produce new algorithmic techniques, as well as deeper understanding of the adequacy of standard performance enhancement tools, like caching and load balancing, for improving energy efficiency. The study on energy complexity will also shed some light on the relation between computation and energy.Some algorithms developed in the course of this research will be implemented, tested empirically on the FreeBSD-based platform, and made available to practitioners. The educational component includes research projects for graduate and undergraduate students, and developing a course on sustainable computing.

能量消耗现在正在成为计算机系统中的主要性能度量。近年来，通过更好的硬件设计和软件工具的结合，在提高能源效率方面取得了重大进展。然而，未来节能计算机系统的设计最终需要开发可用于指导实际解决方案的基本模型和算法工具。本项目旨在研究提高计算机系统数据处理和存储能效的算法方法。基本方法是以组合优化的语言对各种系统组件的操作进行建模，目标函数代表能耗，并使用精确或近似的有效算法来解决这些问题。这些问题中的许多都可以用任务调度来表示，其目标是优化完成一系列任务所需的CPU能耗，同时满足某些性能要求。其他示例包括通过优化功率水平和复杂的分页或高速缓存策略来最小化存储器系统（内部存储器和外部存储器两者）的能量消耗。除了解决一些具体的能源优化问题，这项工作预计将产生新的算法技术，以及更深入地了解标准的性能增强工具，如缓存和负载平衡，以提高能源效率的充分性。对能量复杂度的研究也将揭示计算和能量之间的关系。在本研究过程中开发的一些算法将在基于FreeBSD的平台上实现，经验测试，并提供给从业者。教育部分包括为研究生和本科生开展的研究项目，以及开发一门关于可持续计算的课程。