AF: Small: Green Computing Algorithmics

权力的Ascension，加入时间和空间，在稀缺的计算资源的顶级是相对最近的。在过去的几十年里，当时间和空间是关键的计算资源时，计算机科学研究人员开发了许多算法技术来设计时间和空间有效的算法，并分析简单模型上特定算法所需的时间和空间。这项研究的直接目标是建立一个广泛适用的算法设计和分析技术的工具包，其中能量，功率和温度是关键资源的算法问题。这将需要发现新的算法技术，因为这些与能源相关的资源具有与时间和空间资源不同的物理特性。节能计算和电源管理是绿色计算中的两个重要焦点领域，绿色计算是对计算机、服务器和相关子系统的设计、制造、使用和处置的研究和实践，高效和有效地对环境的影响最小。目前，信息技术的能源使用量与航空业的能源使用量大致相当。一方面，鉴于信息技术的增长仍然是指数级的，如果信息技术不发生变化，这一比例可能会大幅增长。另一方面，考虑到能源效率直到最近还不是一阶设计约束，信息技术的能源效率的数量级改进是可能的。本研究的长期目标是建立一门算法功率管理科学，以支持计算社区抽象功率、能量和温度的能力。 这门科学将服务于这些软件工程师，当他们面对信息技术设计问题时，其中功率/能量/温度是关键的稀缺资源，就像算法科学在他们关注时间和空间资源时为他们服务一样。

The ascension of power, to join time and space, in the top ranks of scarce computational resources is relatively recent. Over the last several decades, when time and space were the key computational resources, computer science researchers developed many algorithmic techniques for designing time and space efficient algorithms, and for analyzing the time and space required by particular algorithms on simple models. The immediate goal of this research is to build a toolkit of widely applicable algorithmic design and analysis techniques for algorithmic problems where energy, power and temperature are the key resources. This will require the discovery of new algorithmic techniques, as these energy related resources have different physical characteristics than the resources of time and space.Energy-efficient computing and power management are two important focus areas within green computing, which is the study and practice of designing, manufacturing, using, and disposing of computers, servers, and associated subsystems, efficiently and effectively with minimal impact on the environment. Currently the energy usage of information technology is roughly comparable to that used by the airline industry. On one hand, given that the growth of information technology is still exponential, this fraction could grow significantly if there are no changes in information technology. On the other hand, given that until recently energy efficiency wasn't a first order design constraint, an order of magnitude improvements in the energy efficiency of information technology is possible.A long term goal of this research is to build a science of algorithmic power management to support the computing community's ability to abstractly reason about power, energy and temperature. This science will serve these software engineers, when faced with information technology design problems in which power/energy/temperature is the key scarce resource, just as the science of algorithms serves them when they are concerned with the resources of time and space.