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    CUDA-Accelerated ODETLAP : a parallel lossy compression implementation for multidimensional data

    Author
    Benedetti, Daniel N
    View/Open
    172981_Benedetti_rpi_0185N_10414.pdf (3.353Mb)
    Other Contributors
    Franklin, W. Randolph; Radke, Richard J., 1974-; Wozny, M. J. (Michael J.); Fox, Peter A.;
    Date Issued
    2014-08
    Subject
    Computer and systems engineering
    Degree
    MS;
    Terms of Use
    This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.;
    Metadata
    Show full item record
    URI
    https://hdl.handle.net/20.500.13015/1185
    Abstract
    This thesis presents an efficient ODETLAP implementation for the compression of gridded, multidimensional data. As vast quantities of data are collected for geographic information systems, compression allows for the storage and transmission of larger data sets. Techniques that utilize autocorrelation in all data dimensions allow for greater levels of compression but are more computationally intensive. ODETLAP, Overdetermined Laplacian Approximation, uses a subset of points from the original data set to accurately reconstruct the data. As it expands into higher dimensions, ODETLAP is capable of using relationships in data across multiple dimensions. Various parallelization techniques are used to improve computation time, utilizing CUDA for general purpose programming on a graphics processing unit (GPGPU). An efficient ODETLAP implementation was created directly in GPU memory, successfully avoiding the overhead associated with the transfer of data between GPU memory and main memory.;
    Description
    August 2014; School of Engineering
    Department
    Dept. of Electrical, Computer, and Systems Engineering;
    Publisher
    Rensselaer Polytechnic Institute, Troy, NY
    Relationships
    Rensselaer Theses and Dissertations Online Collection;
    Access
    Restricted to current Rensselaer faculty, staff and students. Access inquiries may be directed to the Rensselaer Libraries.;
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    • RPI Theses Online (Complete)

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