{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/46862"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/46862","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Parallel computing on geostatistical data using CUDA","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Shan, Feng"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Hart, John C."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-01-16T18:19:00Z","date_published":"2014-01-16T18:19:00Z","updated_at":"2026-07-22T22:25:38Z","subjects":["Compute Unified Device Architecture (CUDA)","parallel computing","average local variance","angular second moment","entropy","inverse difference moment"],"languages":["en"],"rights":["Copyright 2013 Feng Shan"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/46862","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Hart, John C."]},{"key":"dc:creator","label":"Author","values":["Shan, Feng"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2014-01-16T18:19:00Z","2016-01-16T11:02:27Z","2013-12"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Compute Unified Device Architecture (CUDA)","parallel computing","average local variance","angular second moment","entropy","inverse difference moment"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2013 Feng Shan"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/46862"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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.","Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2013-12-11T16:42:21Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Shan_Feng.pdf: 706017 bytes, checksum: 9f3fdba632d79c952b059ec4be755db8 (MD5)","Made available in DSpace on 2014-01-16T18:19:00Z (GMT). No. of bitstreams: 2 Feng_Shan.pdf: 706017 bytes, checksum: 9f3fdba632d79c952b059ec4be755db8 (MD5) license.txt: 4057 bytes, checksum: b9e1c494da2399a9e6644926b936ffc5 (MD5)","Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by Seth Robbins (robbins.sd@gmail.com) on 2014-01-16T18:19:51Z Item is restricted until 2016-01-16T18:19:34Z","Restriction data tranferred 2014-07-01T11:33:32-05:00 Original Data Group with Access UIUC Users [automated] Release Date: 2016-01-16 12:19:34 UTC Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only Restriction Lifted for Item 46881 on 2016-01-16T11:02:27Z."]},{"key":"dc:title","label":"Title","values":["Parallel computing on geostatistical data using CUDA"]}]}],"canonical_facts":{"dc:contributor":["Hart, John C."],"dc:creator":["Shan, Feng"],"dc:date":["2014-01-16T18:19:00Z","2016-01-16T11:02:27Z","2013-12"],"dc:description":["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.","Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2013-12-11T16:42:21Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Shan_Feng.pdf: 706017 bytes, checksum: 9f3fdba632d79c952b059ec4be755db8 (MD5)","Made available in DSpace on 2014-01-16T18:19:00Z (GMT). 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