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Acceleration and execution of relational queries using general purpose graphics processing unit (GPGPU)

This 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.
Date07 January 2016
CreatorsWu, Haicheng
ContributorsYalamanchili, Sudhakar
PublisherGeorgia Institute of Technology
Source SetsGeorgia Tech Electronic Thesis and Dissertation Archive
Detected LanguageEnglish

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