Lattice Boltzmann liquid simulations on graphics hardware

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dc.contributor.advisorGain, Jamesen_ZA
dc.contributor.advisorKuttel, Michelle Maryen_ZA
dc.contributor.authorClough, Duncanen_ZA
dc.date.accessioned2014-11-05T03:57:35Z
dc.date.available2014-11-05T03:57:35Z
dc.date.issued2014en_ZA
dc.descriptionIncludes bibliographical referencesen_ZA
dc.description.abstractFluid simulation is widely used in the visual effects industry. The high level of detail required to produce realistic visual effects requires significant computation. Usually, expensive computer clusters are used in order to reduce the time required. However, general purpose Graphics Processing Unit (GPU) computing has potential as a relatively inexpensive way to reduce these simulation times. In recent years, GPUs have been used to achieve enormous speedups via their massively parallel architectures. Within the field of fluid simulation, the Lattice Boltzmann Method (LBM) stands out as a candidate for GPU execution because its grid-based structure is a natural fit for GPU parallelism. This thesis describes the design and implementation of a GPU-based free-surface LBM fluid simulation. Broadly, our approach is to ensure that the steps that perform most of the work in the LBM (the stream and collide steps) make efficient use of GPU resources. We achieve this by removing complexity from the core stream and collide steps and handling interactions with obstacles and tracking of the fluid interface in separate GPU kernels. To determine the efficiency of our design, we perform separate, detailed analyses of the performance of the kernels associated with the stream and collide steps of the LBM. We demonstrate that these kernels make efficient use of GPU resources and achieve speedups of 29.6_ and 223.7_, respectively. Our analysis of the overall performance of all kernels shows that significant time is spent performing obstacle adjustment and interface movement as a result of limitations associated with GPU memory accesses. Lastly, we compare our GPU LBM implementation with a single-core CPU LBM implementation. Our results show speedups of up to 81.6_ with no significant differences in output from the simulations on both platforms. We conclude that order of magnitude speedups are possible using GPUs to perform free-surface LBM fluid simulations, and that GPUs can, therefore, significantly reduce the cost of performing high-detail fluid simulations for visual effects.en_ZA
dc.identifier.apacitationClough, D. (2014). <i>Lattice Boltzmann liquid simulations on graphics hardware</i>. (Thesis). University of Cape Town ,Faculty of Science ,Department of Computer Science. Retrieved from http://hdl.handle.net/11427/9206en_ZA
dc.identifier.chicagocitationClough, Duncan. <i>"Lattice Boltzmann liquid simulations on graphics hardware."</i> Thesis., University of Cape Town ,Faculty of Science ,Department of Computer Science, 2014. http://hdl.handle.net/11427/9206en_ZA
dc.identifier.citationClough, D. 2014. Lattice Boltzmann liquid simulations on graphics hardware. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Clough, Duncan AB - Fluid simulation is widely used in the visual effects industry. The high level of detail required to produce realistic visual effects requires significant computation. Usually, expensive computer clusters are used in order to reduce the time required. However, general purpose Graphics Processing Unit (GPU) computing has potential as a relatively inexpensive way to reduce these simulation times. In recent years, GPUs have been used to achieve enormous speedups via their massively parallel architectures. Within the field of fluid simulation, the Lattice Boltzmann Method (LBM) stands out as a candidate for GPU execution because its grid-based structure is a natural fit for GPU parallelism. This thesis describes the design and implementation of a GPU-based free-surface LBM fluid simulation. Broadly, our approach is to ensure that the steps that perform most of the work in the LBM (the stream and collide steps) make efficient use of GPU resources. We achieve this by removing complexity from the core stream and collide steps and handling interactions with obstacles and tracking of the fluid interface in separate GPU kernels. To determine the efficiency of our design, we perform separate, detailed analyses of the performance of the kernels associated with the stream and collide steps of the LBM. We demonstrate that these kernels make efficient use of GPU resources and achieve speedups of 29.6_ and 223.7_, respectively. Our analysis of the overall performance of all kernels shows that significant time is spent performing obstacle adjustment and interface movement as a result of limitations associated with GPU memory accesses. Lastly, we compare our GPU LBM implementation with a single-core CPU LBM implementation. Our results show speedups of up to 81.6_ with no significant differences in output from the simulations on both platforms. We conclude that order of magnitude speedups are possible using GPUs to perform free-surface LBM fluid simulations, and that GPUs can, therefore, significantly reduce the cost of performing high-detail fluid simulations for visual effects. DA - 2014 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2014 T1 - Lattice Boltzmann liquid simulations on graphics hardware TI - Lattice Boltzmann liquid simulations on graphics hardware UR - http://hdl.handle.net/11427/9206 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/9206
dc.identifier.vancouvercitationClough D. Lattice Boltzmann liquid simulations on graphics hardware. [Thesis]. University of Cape Town ,Faculty of Science ,Department of Computer Science, 2014 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/9206en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Computer Scienceen_ZA
dc.publisher.facultyFaculty of Scienceen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.titleLattice Boltzmann liquid simulations on graphics hardwareen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMScen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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