Skip to content
University of Maryland LibrariesDigital Repository at the University of Maryland
    • Login
    View Item 
    •   DRUM
    • Theses and Dissertations from UMD
    • UMD Theses and Dissertations
    • View Item
    •   DRUM
    • Theses and Dissertations from UMD
    • UMD Theses and Dissertations
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Characterizing and Accelerating Bioinformatics Workloads on Modern Microarchitectures

    Thumbnail
    View/Open
    umi-umd-4316.pdf (1.270Mb)
    No. of downloads: 1533

    Date
    2007-04-25
    Author
    Albayraktaroglu, Kursad
    Advisor
    Franklin, Manoj
    Metadata
    Show full item record
    Abstract
    Bioinformatics, the use of computer techniques to analyze biological data, has been a particularly active research field in the last two decades. Advances in this field have contributed to the collection of enormous amounts of data, and the sheer amount of available data has started to overtake the processing capability possible with current computer systems. Clearly, computer architects need to have a better understanding of how bioinformatics applications work and what kind of architectural techniques could be used to accelerate these important scientific workloads on future processors. In this dissertation, we develop a bioinformatic benchmark suite and provide a detailed characterization of these applications in common use today from a computer architect's point of view. We analyze a wide range of detailed execution characteristics including instruction mix, IPC measurements, L1 and L2 cache misses on a real architecture; and proceed to analyze the workloads' memory access characteristics. We then concentrate on accelerating a particularly computationally intensive bioinformatics workload on the novel Cell Broadband Engine multiprocessor architecture. The HMMER workload is used for protein profile searching using hidden Markov models, and most of its execution time is spent running the Viterbi algorithm. We parallelize and partition the HMMER application to implement it on the Cell Broadband Engine. In order to run the Viterbi algorithm on the 256KB local stores of the Cell BE synergistic processing units (SPEs), we present a method to develop a fast SIMD implementation of the Viterbi algorithm that reduces the storage requirements significantly. Our HMMER implementation for the Cell BE architecture, Cell-HMMER, exploits the multiple levels of parallelism inherent in this application, and can run protein profile searches up to 27.98 times faster than a modern dual-core x86 microprocessor.
    URI
    http://hdl.handle.net/1903/6828
    Collections
    • Electrical & Computer Engineering Theses and Dissertations
    • UMD Theses and Dissertations

    DRUM is brought to you by the University of Maryland Libraries
    University of Maryland, College Park, MD 20742-7011 (301)314-1328.
    Please send us your comments.
    Web Accessibility
     

     

    Browse

    All of DRUMCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    LoginRegister
    Pages
    About DRUMAbout Download Statistics

    DRUM is brought to you by the University of Maryland Libraries
    University of Maryland, College Park, MD 20742-7011 (301)314-1328.
    Please send us your comments.
    Web Accessibility