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Accelerating the similarity self-join using the GPU
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DOI：10.1016/j.jpdc.2019.06.005.png)
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Abstract
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The self-join finds all objects in a dataset within a threshold of each other defined by a similarity metric. As such, the self-join is a fundamental building block for the field of databases and data mining. In low dimensionality, there are several challenges associated with efficiently computing the self-join on the graphics processing unit (GPU). Low dimensional data results in higher data densities, causing a significant number of distance calculations and a large result set, and as dimensionality increases, index searches become increasingly exhaustive. We propose several techniques to optimize the self-join using the GPU that include a CPU-efficient index that employs a bounded search, a batching scheme to accommodate large result sets, and duplicate search removal with low overhead. Furthermore, we propose a performance model that reveals bottlenecks related to the result set size and enables us to choose a batch size that mitigates two sources of performance degradation. Our approach outperforms the state-of-the-art on most scenarios. (C) 2019 Elsevier Inc. All rights reserved.
Keywords:
GPGPU
In-memory database
Query optimization
Self-join
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