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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.04349 |
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| _version_ | 1866929628333998080 |
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| author | Zhu, Xiaoke Xie, Min Deng, Ting Zhang, Qi |
| author_facet | Zhu, Xiaoke Xie, Min Deng, Ting Zhang, Qi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_04349 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | HyperBlocker: Accelerating Rule-based Blocking in Entity Resolution using GPUs Zhu, Xiaoke Xie, Min Deng, Ting Zhang, Qi Databases H.2 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. |
| title | HyperBlocker: Accelerating Rule-based Blocking in Entity Resolution using GPUs |
| topic | Databases H.2 |
| url | https://arxiv.org/abs/2410.04349 |