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Accelerating Entity Resolution Through Vectorized Meta-blocking on GPUs
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AI
DOI：10.1007/978-3-032-05727-3_14.png)
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Abstract
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This paper presents an approach to accelerate the Metablocking phase in entity resolution (ER) by leveraging GPU computational power. We enhance the performance of conventional meta-blocking algorithms by utilizing sparse matrix representations of block collections. Our proposed solution remains orthogonal to existing blocking and matching techniques, ensuring that their effectiveness is not compromised. By converting a standard block collection to a one-hot encoded sparse matrix and implementing block purging, block filtering, and edge pruning on GPUs, we achieve up to 40.x speedups compared to CPUbased implementations.
Keywords:
Entity resolution
Data integration
Blocking
Meta-blocking
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