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An infrastructure for program power behavior characterization and optimization evaluation
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Title
An infrastructure for program power behavior characterization and optimization evaluation
Name
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Hu
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Chunling
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Chunling Hu
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author
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Jiménez
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Daniel
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Advisory Committee
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Daniel Jiménez
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chair
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Kremer
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Ulrich
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Advisory Committee
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Ulrich Kremer
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internal member
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Bianchini
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Ricardo
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Advisory Committee
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Ricardo Bianchini
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internal member
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Liu
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Jack
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Advisory Committee
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Jack Liu
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outside member
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Rutgers University
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degree grantor
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Graduate School - New Brunswick
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theses
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2008
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2008-01
Language
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English
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electronic
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xiii, 108 pages
Abstract
Fine-grained program power behavior is useful in both evaluating power optimizations and observing power optimization opportunities. Detailed power simulation is time consuming and often inaccurate. Physical power measurement is faster and objective. However, fine-grained measurement generates enormous amounts of data in which locating important features is difficult, while coarse-grained measurement sacrifices important detail.
This thesis presents a program power behavior characterization infrastructure that identifies program phases, selects a representative interval of execution for each phase, and instruments the program to enable precise and objective power measurement of these intervals to get their time-dependent power behavior. This infrastructure is constituted of three components for instrumentation, phase classification and power measurement, respectively. The Camino compiler, a GCC post-processor, is used to instrument the assembly code of a program on various levels. A phase classification algorithm using infrequent basic blocks and the combination of control-flow information and runtime event counts finds the representative intervals in terms of time-dependent power behavior. These selected intervals accurately characterize the fine-grained time-dependent behavior of the program, as well as accurately
estimate the total energy consumption of a program. The power measurement method enables users to measure any specified region of program execution. A two-level profiling method implemented in this infrastructure maps the measured detailed power behavior back to source code. The accuracy of this infrastructure is validated on a StrongARM SA110 through simulation, and on an Intel Pentium 4 system through physical power measurement.
This thesis also presents the uses of this infrastructure in understanding the power behavior of program components, such as procedures or loops, in finding good threshold for metrics used in dynamic voltage and frequency scaling, and in scheduling simultaneous multi-threaded programs for peak power optimization.
This thesis presents a program power behavior characterization infrastructure that identifies program phases, selects a representative interval of execution for each phase, and instruments the program to enable precise and objective power measurement of these intervals to get their time-dependent power behavior. This infrastructure is constituted of three components for instrumentation, phase classification and power measurement, respectively. The Camino compiler, a GCC post-processor, is used to instrument the assembly code of a program on various levels. A phase classification algorithm using infrequent basic blocks and the combination of control-flow information and runtime event counts finds the representative intervals in terms of time-dependent power behavior. These selected intervals accurately characterize the fine-grained time-dependent behavior of the program, as well as accurately
estimate the total energy consumption of a program. The power measurement method enables users to measure any specified region of program execution. A two-level profiling method implemented in this infrastructure maps the measured detailed power behavior back to source code. The accuracy of this infrastructure is validated on a StrongARM SA110 through simulation, and on an Intel Pentium 4 system through physical power measurement.
This thesis also presents the uses of this infrastructure in understanding the power behavior of program components, such as procedures or loops, in finding good threshold for metrics used in dynamic voltage and frequency scaling, and in scheduling simultaneous multi-threaded programs for peak power optimization.
Note
(type = degree)
Ph.D.
Note
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Includes bibliographical references (p. 101-106).
Subject
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Topic
Computer Science
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Topic
Computer architecture
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Graduate School - New Brunswick Electronic Theses and Dissertations
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rucore19991600001
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http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.17138
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ETD_347
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NjNbRU
Identifier
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doi:10.7282/T3BC3ZWC
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ETD doctoral
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Rights
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Copyright
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Copyright protected
Availability
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Open
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Name
Chunling Hu
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Copyright holder
Affiliation
Rutgers University. Graduate School - New Brunswick
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Non-exclusive ETD license
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Author Agreement License
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I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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