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HyperBlocker: Accelerating Rule-based Blocking in Entity Resolution using GPUs
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AI
DOI：10.14778/3705829.3705847.png)
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Abstract
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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 data- aware 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 real-life 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.
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556
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