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Title:Parallel computing on geostatistical data using CUDA
Author(s):Shan, Feng
Advisor(s):Hart, John C.
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Compute Unified Device Architecture (CUDA)
parallel computing
average local variance
angular second moment
inverse difference moment
Abstract:Data analysis is receiving considerable attention with the design of new graphics processing units (GPUs). Our study focuses on geostatistical data analysis, which is currently applied in diverse disciplines such as meteorology, oceanography, geography, forestry, environmental control, and agriculture. While geostatistical analysis algorithms are applied in varied branches, those analyses can be accelerated by applying parallel computing using modern GPUs. The highly parallel structure makes modern GPUs more effective than general-purpose CPUs for algorithms where processing of large blocks of data is done in parallel. In our study, we compared the performance between serial and parallel computation on four texture features, including average local variance (ALV), angular second moment (ASM), entropy, and inverse difference moment (IDM). The later three features (ASM, Entropy and IDM) are features obtained using Gray Level Coocurrence Matrices (GLCM). We parallelized the computation by using multiple sliding windows on two-dimensional data concurrently. Our approach also includes, in addition to comparing to serial implementation, measuring the parallelized performance under different data sizes. As a result, parallel computation on geostatistical analyses using GPU can significantly increase the performance and efficiency. It has also demonstrated the possibility to provide solutions for specific needs by reducing the time of computation.
Issue Date:2014-01-16
Rights Information:Copyright 2013 Feng Shan
Date Available in IDEALS:2014-01-16
Date Deposited:2013-12

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