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    OPTIMIZING PROCESSOR AND MEMORY FOR GREEN COMPUTING

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    azu_etd_11951_sip1_m.pdf
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    Author
    Bi, Mingsong
    Issue Date
    2011
    Keywords
    OS
    Processor
    Computer Science
    Energy
    Memory
    Advisor
    Gniady, Christopher
    
    Metadata
    Show full item record
    Publisher
    The University of Arizona.
    Rights
    Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
    Embargo
    Embargo: Release after 12/12/2012
    Abstract
    Energy efficiency has become one of the most important factors in the development of computer systems. Increasingly power-hungry processors and memory subsystem have reinforced the need for aggressive power management. Dynamic voltage scaling has become a common consideration for designing energy efficient CPUs in systems ranging from portable devices to large-scale systems. As applications become more data centric and put more pressure on memory subsystem, managing energy consumption of main memory is also becoming critical. Subsequently in this dissertation, we address the issues in designing energy efficient CPU and memory for personal computing devices as well as large-scale systems.For large-scale systems, we address memory subsystem dedicated to buffer cache which accounts for the majority of memory usage in server environment. We take advantage of the I/O handling routines in the OS kernel to hide the delay incurred by the memory state transition so that performance degradation is minimized while high energy savings is achieved. We also address interactive workloads, which account for the bulk of the processing demand on modern mobile or desktop systems. We propose Interaction-Aware Dynamic Voltage Scaling (IADVS) for CPU and Interaction-Aware Memory Energy Management (IAMEM) for memory. The IA framework relies on automatic correlation of user-initiated tasks with the demand placed on CPU and memory to accurately predict power states for CPU and memory. Both mechanisms achieve maximal energy savings while minimizing the impact on the application's performance.
    Type
    text
    Electronic Dissertation
    Degree Name
    Ph.D.
    Degree Level
    doctoral
    Degree Program
    Graduate College
    Computer Science
    Degree Grantor
    University of Arizona
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