Author
Benedetti, Daniel N
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.;
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.;
