Accelerating geographic processing using GPUs: Implemented in OpenCL

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dc.contributor.authorJaballah, Alexander
dc.contributor.authorMohlin, Rafael
dc.contributor.departmentChalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers)sv
dc.contributor.departmentChalmers University of Technology / Department of Computer Science and Engineering (Chalmers)en
dc.date.accessioned2019-07-03T14:35:54Z
dc.date.available2019-07-03T14:35:54Z
dc.date.issued2017
dc.description.abstractIn This thesis, we present how a geographical process can be increased in execution time and asymptotic complexity, by moving the processing algorithm from the Central Processing unit (CPU) to the Graphics Processing Unit (GPU). We also investigate different memory strategies on the GPU in order to further decrease the execution time for the algorithm. To improve the asymptotic complexity two new algorithms are investigated and implemented, the first algorithm is based on the concept of separability, and the second algorithm is a state-of-the-art algorithm called Gaussian filter kernel. The outcome of our work is an approach of how algorithms on the CPU that has great potential of parallelism can be moved to the GPU to improve the execution time. In order to evaluate the different algorithms, tests regarding the execution time and outcome accuracy were conducted. Lastly, we concluded the overall success of the improvement regarding the execution time and reduction for the asymptotic complexity.
dc.identifier.urihttps://hdl.handle.net/20.500.12380/251608
dc.language.isoeng
dc.setspec.uppsokTechnology
dc.subjectData- och informationsvetenskap
dc.subjectComputer and Information Science
dc.titleAccelerating geographic processing using GPUs: Implemented in OpenCL
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster Thesisen
dc.type.uppsokH
local.programmeSoftware engineering and technology (MPSOF), MSc
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