{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/44803"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/44803","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"OpenMP-CUDA implementation of the moment method and multilevel fast multipole algorithm on multi-GPU computing systems","abstract":"In this thesis, the method of moments (MoM) and the multilevel fast multipole algorithm (MLFMA) are implemented for GPU computation based on the hybrid OpenMP-CUDA parallel programming model. The resultant algorithms are called the OpenMP-CUDA-MoM and the OpenMP-CUDA-MLFMA, respectively. Both of the proposed methods are applied to compute electromagnetic scattering by a three-dimensional conducting object. For the OpenMP-CUDA-MoM, the multi-GPU parallelization of system matrix assembly, iterative solution, and fast evaluation of radar cross section (RCS) are discussed in detail. The parallel efficiency versus number of devices is investigated through the calculation of a conducting sphere on different number of GPUs. The parallel efficiency of the total computation is over 87%. The total speedup for the monostatic RCS calculation of a NASA almond by 4 GPUs is between 80 and 260 times. For the GPU accelerated MLFMA, the hierarchical parallelization strategy is employed, which ensures a high computational throughput for the GPU calculation. The resulting OpenMP-based multi-GPU implementation is capable of solving real-life problems with over 1 million unknowns with a remarkable speedup. The RCS of a few benchmark objects are calculated to demonstrate the accuracy of the solution. The results are compared with those from the CPU-based MLFMA and measurements. The capability of the proposed method is analyzed through the examples of a sphere, an aerocraft and a missile-like object. The total speedup achieved by 4 GPUs is between 20 and 80 times.","abstract_html":"In this thesis, the method of moments (MoM) and the multilevel fast multipole algorithm (MLFMA) are implemented for GPU computation based on the hybrid OpenMP-CUDA parallel programming model. The resultant algorithms are called the OpenMP-CUDA-MoM and the OpenMP-CUDA-MLFMA, respectively. Both of the proposed methods are applied to compute electromagnetic scattering by a three-dimensional conducting object. For the OpenMP-CUDA-MoM, the multi-GPU parallelization of system matrix assembly, iterative solution, and fast evaluation of radar cross section (RCS) are discussed in detail. The parallel efficiency versus number of devices is investigated through the calculation of a conducting sphere on different number of GPUs. The parallel efficiency of the total computation is over 87%. The total speedup for the monostatic RCS calculation of a NASA almond by 4 GPUs is between 80 and 260 times. For the GPU accelerated MLFMA, the hierarchical parallelization strategy is employed, which ensures a high computational throughput for the GPU calculation. The resulting OpenMP-based multi-GPU implementation is capable of solving real-life problems with over 1 million unknowns with a remarkable speedup. The RCS of a few benchmark objects are calculated to demonstrate the accuracy of the solution. The results are compared with those from the CPU-based MLFMA and measurements. The capability of the proposed method is analyzed through the examples of a sphere, an aerocraft and a missile-like object. The total speedup achieved by 4 GPUs is between 20 and 80 times.","abstract_has_math":false,"creators":["Guan, Jian"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Jin, Jianming"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013-05-28T19:20:32Z","date_published":"2013-05-28T19:20:32Z","updated_at":"2026-07-22T22:25:34Z","subjects":["CUDA","electromagnetic scattering","hybrid parallel programming model","moment method","multilevel fast multipole algorithm","multi-GPU","OpenMP","radar cross section"],"languages":["en"],"rights":["Copyright 2013 Jian Guan"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/44803","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Jin, Jianming"]},{"key":"dc:creator","label":"Author","values":["Guan, Jian"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2013-05-28T19:20:32Z","2015-05-28T10:01:32Z","2013-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"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":["CUDA","electromagnetic scattering","hybrid parallel programming model","moment method","multilevel fast multipole algorithm","multi-GPU","OpenMP","radar cross section"]}]},{"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 Jian Guan"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/44803"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["In this thesis, the method of moments (MoM) and the multilevel fast multipole algorithm (MLFMA) are implemented for GPU computation based on the hybrid OpenMP-CUDA parallel programming model. The resultant algorithms are called the OpenMP-CUDA-MoM and the OpenMP-CUDA-MLFMA, respectively. Both of the proposed methods are applied to compute electromagnetic scattering by a three-dimensional conducting object. For the OpenMP-CUDA-MoM, the multi-GPU parallelization of system matrix assembly, iterative solution, and fast evaluation of radar cross section (RCS) are discussed in detail. The parallel efficiency versus number of devices is investigated through the calculation of a conducting sphere on different number of GPUs. The parallel efficiency of the total computation is over 87%. The total speedup for the monostatic RCS calculation of a NASA almond by 4 GPUs is between 80 and 260 times. For the GPU accelerated MLFMA, the hierarchical parallelization strategy is employed, which ensures a high computational throughput for the GPU calculation. The resulting OpenMP-based multi-GPU implementation is capable of solving real-life problems with over 1 million unknowns with a remarkable speedup. The RCS of a few benchmark objects are calculated to demonstrate the accuracy of the solution. The results are compared with those from the CPU-based MLFMA and measurements. The capability of the proposed method is analyzed through the examples of a sphere, an aerocraft and a missile-like object. The total speedup achieved by 4 GPUs is between 20 and 80 times.","Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2013-04-03T18:36:12Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Guan_Jian.pdf: 4918675 bytes, checksum: 4b0dae601bd7352535999a25ce975591 (MD5)","Made available in DSpace on 2013-05-28T19:20:32Z (GMT). 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The resultant algorithms are called the OpenMP-CUDA-MoM and the OpenMP-CUDA-MLFMA, respectively. Both of the proposed methods are applied to compute electromagnetic scattering by a three-dimensional conducting object. For the OpenMP-CUDA-MoM, the multi-GPU parallelization of system matrix assembly, iterative solution, and fast evaluation of radar cross section (RCS) are discussed in detail. The parallel efficiency versus number of devices is investigated through the calculation of a conducting sphere on different number of GPUs. The parallel efficiency of the total computation is over 87%. The total speedup for the monostatic RCS calculation of a NASA almond by 4 GPUs is between 80 and 260 times. For the GPU accelerated MLFMA, the hierarchical parallelization strategy is employed, which ensures a high computational throughput for the GPU calculation. The resulting OpenMP-based multi-GPU implementation is capable of solving real-life problems with over 1 million unknowns with a remarkable speedup. The RCS of a few benchmark objects are calculated to demonstrate the accuracy of the solution. The results are compared with those from the CPU-based MLFMA and measurements. The capability of the proposed method is analyzed through the examples of a sphere, an aerocraft and a missile-like object. The total speedup achieved by 4 GPUs is between 20 and 80 times.","Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2013-04-03T18:36:12Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Guan_Jian.pdf: 4918675 bytes, checksum: 4b0dae601bd7352535999a25ce975591 (MD5)","Made available in DSpace on 2013-05-28T19:20:32Z (GMT). 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