STUDENT PAPER: GPU Acceleration for SQL Queries on Large-Scale Distributed Systems

Volume 8, Issue 1 (February 2017), pp. 20–26

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@article{jocse-8-1-5,
  author={Linh Nguyen and Paul Hemler},
  title={STUDENT PAPER: GPU Acceleration for SQL Queries on Large-Scale Distributed Systems},
  journal={The Journal of Computational Science Education},
  year=2017,
  month=feb,
  volume=8,
  issue=1,
  pages={20--26},
  doi={https://doi.org/10.22369/issn.2153-4136/8/1/5}
}

General purpose GPUs are a powerful hardware with a number of applications in the realm of relational databases. We further extended a database framework designed to allow for GPU execution queries. Our technique is novel in that it implements Dynamic Parallelism, a new feature in recent hardware, to accelerate SQL JOINs. Query execution results in 1.25X speedup on average with respect to a previous method, also accelerated by GPUs, which employs a multi-dimensional CUDA Grid. More importantly, we divided the queries to run on multiple BW nodes to investigate the scalability of both SELECT and JOIN.