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.

National Science Foundation (NSF)
Division of Computer and Communication Foundations (CCF)
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Balasubramanian Kalyanasundaram
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University of Pittsburgh
United States
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