HyperBlocker: Accelerating Rule-based Blocking in Entity Resolution using GPUs
<|reference_start|>HyperBlocker: Accelerating Rule-based Blocking in Entity Resolution using GPUs: This paper studies rule-based blocking in Entity Resolution (ER). We propose HyperBlocker, a GPU-accelerated system for blocking in ER. As opposed to previous blocking algorithms and parallel blocking solvers, HyperBlocker employs a pipelined architecture to overlap data transfer and GPU operations. It generates a dataaware and rule-aware execution plan on CPUs, for specifying how rules are evaluated, and develops a number of hardware-aware optimizations to achieve massive parallelism on GPUs. Using reallife datasets, we show that HyperBlocker is at least 6.8x and 9.1x faster than prior CPU-powered distributed systems and GPU-based ER solvers, respectively. Better still, by combining HyperBlocker with the state-of-the-art ER matcher, we can speed up the overall ER process by at least 30% with comparable accuracy.<|reference_end|>
@article{zhu2024hyperblocker:,
    title={HyperBlocker: Accelerating Rule-based Blocking in Entity Resolution
  using GPUs},
    author={Xiaoke Zhu, Min Xie, Ting Deng, Qi Zhang},
    journal={arXiv preprint arXiv:2410.04349},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.04349},
    primaryClass={cs.DB}
}