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    An Efficient Architecture for Dynamic Profiling of Multicore Systems

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    Author
    Sargur, Sudarshan Lakshminarasimhan
    Issue Date
    2015
    Keywords
    Hardware/Software Partitioning
    Multicore
    Profiling
    Electrical & Computer Engineering
    Dynamic Optimization
    Advisor
    Lysecky, Roman
    
    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.
    Abstract
    Application profiling is an important step in the design and optimization of embedded systems. Accurately identifying and analyzing the execution of frequently executed computational kernels is needed to effectively optimize the system implementation, both at design time and runtime. In a traditional design process, it suffices to perform the profiling and optimization steps offline, during design time. The offline profiling guides the design space exploration, hardware software codesign, or power and performance optimizations. When the system implementation can be finalized at design time, this approach works well. However, dynamic optimization techniques, which adapt and reconfigure the system at runtime, require dynamic profiling with minimum runtime overheads. Existing profiling methods are usually software based and incur significant overheads that may be prohibitive or impractical for profiling embedded systems at runtime. In addition, these profiling methods typically focus on profiling the execution of specific tasks executing on a single processor core, but do not consider accurate and holistic profiling across multiple processor cores. Directly utilizing existing profiling approaches and naively combining isolated profiles from multiple processor cores can lead to significant profile inaccuracies of up to 35%. To address these challenges, a hardware-based dynamic application profiler for non-intrusively and accurately profiling software applications in multicore embedded systems is presented. The profiler provides a detailed execution profile for computational kernels and maintains profile accuracy across multiple processor cores. The hardware-based profiler achieves an average error of less than 0.5% for the percentage execution time of profiled applications while being area efficient.
    Type
    text
    Electronic Thesis
    Degree Name
    M.S.
    Degree Level
    masters
    Degree Program
    Graduate College
    Electrical & Computer Engineering
    Degree Grantor
    University of Arizona
    Collections
    Master's Theses

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