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

Abstract

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.