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dc.contributor.advisorYalamanchili, Sudhakar
dc.contributor.authorWu, Haicheng
dc.date.accessioned2016-01-07T17:36:04Z
dc.date.available2016-01-07T17:36:04Z
dc.date.created2015-12
dc.date.issued2015-11-16
dc.date.submittedDecember 2015
dc.identifier.urihttp://hdl.handle.net/1853/54405
dc.description.abstractThis thesis first maps the relational computation onto Graphics Processing Units (GPU)s by designing a series of tools and then explores the different opportunities of reducing the limitation brought by the memory hierarchy across the CPU and GPU system. First, a complete end-to-end compiler and runtime infrastructure, Red Fox, is proposed. The evaluation on the full set of industry standard TPC-H queries on a single node GPU shows on average Red Fox is 11.20x faster compared with a commercial database system on a state of art CPU machine. Second, a new compiler technique called kernel fusion is designed to fuse the code bodies of several relational operators to reduce data movement. Third, a multi-predicate join algorithm is designed for GPUs which can provide much better performance and be used with more flexibility compared with kernel fusion. Fourth, the GPU optimized multi-predicate join is integrated into a multi-threaded CPU database runtime system that supports out-of-core data set to solve real world problem. This thesis presents key insights, lessons learned, measurements from the implementations, and opportunities for further improvements.
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherGeorgia Institute of Technology
dc.subjectDatabase
dc.subjectGPU
dc.titleAcceleration and execution of relational queries using general purpose graphics processing unit (GPGPU)
dc.typeDissertation
dc.description.degreePh.D.
dc.contributor.departmentElectrical and Computer Engineering
thesis.degree.levelDoctoral
dc.contributor.committeeMemberKim, Hyesoon
dc.contributor.committeeMemberWills, Linda
dc.contributor.committeeMemberVuduc, Richard
dc.contributor.committeeMemberPande, Santosh
dc.date.updated2016-01-07T17:36:04Z


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