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• W2893811495 abstract "The similarity self-join finds all objects in a dataset that are within a search distance, ∈, of each other. As such, the self-join is a building block of many algorithms. In high dimensions, indexing structures become increasingly ineffective at pruning the search, making the self-join challenging to compute efficiently. We advance a GPU-accelerated self-join algorithm targeted towards high dimensional data. The massive parallelism afforded by the GPU and high aggregate memory bandwidth makes the architecture well-suited for data-intensive workloads. We leverage a grid-based GPU-tailored index to perform range queries, and propose the following optimizations: (i) a trade-off between candidate set filtering and index search overhead by exploiting properties of the index; (ii) reordering the data based on variance in each dimension to improve the filtering power of the index; and (iii) a pruning method for reducing the number of expensive distance calculations. Our algorithm generally outperforms a parallel CPU state-of-the-art approach." @default.
• W2893811495 created "2018-10-05" @default.
• W2893811495 creator A5057388897 @default.
• W2893811495 creator A5075564331 @default.
• W2893811495 date "2019-07-01" @default.
• W2893811495 modified "2023-10-16" @default.
• W2893811495 title "GPU-Accelerated Similarity Self-Join for Multi-Dimensional Data" @default.
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• W2893811495 doi "https://doi.org/10.1145/3329785.3329920" @default.
• W2893811495 hasPublicationYear "2019" @default.
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