Digital information management is the key enabler for unprecedented rise in productivity and efficiency gains experienced by the world economies during the 21st century. Information processing systems have thus become essential to the functioning of business, service, academic, and governmental institutions. As institutions increase their offerings of digital information services, the demand for computation and storage capability also increases. Examples include online banking, e-filing of taxes, music and video downloads, online shipment tracking, real-time inventory/supply-chain management, electronic medical recording, insurance database management, surveillance and disaster recovery. It is estimated that, in some industries, the number of records that must be retained is growing at a CAGR of 50 percent or greater. This exponential increase in the digital intensity of human existence is driven by many factors, including ease of use and availability of a rich set of information technology (IT) devices and services. Indeed, it would be difficult to imagine how significant societal transformations that better our world could occur without the productivity and innovation enabled by the IT. Unfortunately, the energy cost and carbon footprint of the IT devices and services has become exorbitant. Moreover, current technological and digital service utilization trends result in a doubling of the energy cost of the IT infrastructure and its carbon footprint in less than five years. In an energy-constrained world, this consumption trend is unsustainable and comes at increasingly unacceptable societal and environmental costs. In my talk, I will first describe what is meant by green computing and how precisely it can be quantified. Next, I will review energy-efficient computing components based on multi-core processing technology, dynamic voltage and frequency scaling, and power and clock gating. Finally, I will describe techniques for improving performance per Watt of large-scale computing systems (e.g., a data center), including multi-scale dynamic power control, task placement and scheduling, resource virtualization, and application optimizations that explore the ability of key computational kernels to dynamically configure hardware for higher efficiency.

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