The solution of large-scale electromagnetic problems with MLFMA on Single-GPU systems

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buir.advisorErtürk, Vakur Behçet
dc.contributor.authorErkal, Mehmet Fatih
dc.date.accessioned2022-02-01T05:45:02Z
dc.date.available2022-02-01T05:45:02Z
dc.date.copyright2022-01
dc.date.issued2022-01
dc.date.submitted2022-01-31
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (Master's): Bilkent University, Department of Electrical and Electronics Engineering, İhsan Doğramacı Bilkent University, 2022.en_US
dc.descriptionIncludes bibliographical references (leaves 96-101).en_US
dc.description.abstractAdvancements in computer technology introduce many new hardware infrastructures with high-performance processing powers. In recent years, the graphics processing unit (GPU) has been one of the popular choices that have been being used in computational engineering fields because of its massively parallel processing capacity and its easy coding structure compatible with new programming systems. The full-wave solution of large-scale electromagnetic (EM) scattering problems with traditional methods has very dense computational operations, and thus additional hardware accelerations become an indispensable demand, especially for practical and industrial applications. In this context, the GPU implementation of full-wave electromagnetic solvers such as multi-level fast multiple algorithm (MLFMA) has shown a trend in the literature for the last decade. However, the GPUs also have many restrictions and bottlenecks when implementing large-scale EM scattering problems with full-wave solvers. Limited random-access-memory (RAM) capacities and data transmission delays are the major bottlenecks. In this study, we propose a matrix partitioning scheme to overcome the RAM restriction of GPUs to be able to solve electrically large size problems in single-GPU systems with MLFMA while acquiring reasonable accelerations by considering different implementation approaches. For this purpose, the Single-Instruction-Multiple-Data (SIMD) structure of GPU is considered for each stage of the MLFMA to check its compatibility. In addition, different operators of MLFMA are fine-tuned on the GPU scale to minimize the overall effect of data transfer and device latency. The preliminary analyses show that significant time efficiencies can be obtained for the different parts of MLFMA as well as eliminating the RAM restriction. The numerical results demonstrate the overall efficiencies of our proposed solution for the bottlenecks of GPU and also validate the expected accelerations for the solution of large-scale EM problems involving electrically large canonical geometries and real-life targets such as an aircraft and a missile geometry.en_US
dc.description.degreeM.S.en_US
dc.description.statementofresponsibilityby Mehmet Fatih Erkalen_US
dc.format.extentxviii, 114 leaves : illustrations, charts, graphics ; 30 cm.en_US
dc.identifier.itemidB134155
dc.identifier.urihttp://hdl.handle.net/11693/76929
dc.language.isoEnglishen_US
dc.publisherBilkent Universityen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectFast algorithmsen_US
dc.subjectMLFMAen_US
dc.subjectHardware accelerationen_US
dc.subjectGPUen_US
dc.titleThe solution of large-scale electromagnetic problems with MLFMA on Single-GPU systemsen_US
dc.title.alternativeBüyük ölçekli elektromanyetik problemlerin ÇSHÇY ile tekli-GİB sistemlerinde çözümüen_US
dc.typeThesisen_US

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Dept. of Electrical and Electronics Engineering - Master's degree