22578

Parallel acceleration of CPU and GPU range queries over large data sets

Mitchell Nelson, Zachary Sorenson, Joseph M. Myre, Jason Sawin, David Chiu
Department of Computer and Information Sciences, University of St. Thomas, 2115 Summit Ave., 55105 Saint Paul, Minnesota, USA
Journal of Cloud Computing: Advances, Systems and Applications, volume 9, Article number: 44, 2020

@article{nelson2020parallel,

   title={Parallel acceleration of CPU and GPU range queries over large data sets},

   author={Nelson, Mitchell and Sorenson, Zachary and Myre, Joseph M and Sawin, Jason and Chiu, David},

   journal={Journal of Cloud Computing},

   volume={9},

   number={1},

   pages={1–21},

   year={2020},

   publisher={Springer}

}

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Data management systems commonly use bitmap indices to increase the efficiency of querying scientific data. Bitmaps are usually highly compressible and can be queried directly using fast hardware-supported bitwise logical operations. The processing of bitmap queries is inherently parallel in structure, which suggests they could benefit from concurrent computer systems. In particular, bitmap-range queries offer a highly parallel computational problem, and the hardware features of graphics processing units (GPUs) offer an alluring platform for accelerating their execution.In this paper, we present four GPU algorithms and two CPU-based algorithms for the parallel execution of bitmap-range queries. We show that in 98.8% of our tests, using real and synthetic data, the GPU algorithms greatly outperform the parallel CPU algorithms. For these tests, the GPU algorithms provide up to 54.1x speedup and an average speedup of 11.5x over the parallel CPU algorithms. In addition to enhancing performance, augmenting traditional bitmap query systems with GPUs to offload bitmap query processing allows the CPU to process other requests.
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