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cuSLINK: Single-linkage Agglomerative Clustering on the GPU

28 June 2023
Corey J. Nolet
Divye Gala
Alexandre Fender
Mahesh M. Doijade
Joe Eaton
Edward Raff
John Zedlewski
Brad Rees
Tim Oates
ArXiv (abs)PDFHTML
Abstract

In this paper, we propose cuSLINK, a novel and state-of-the-art reformulation of the SLINK algorithm on the GPU which requires only O(Nk)O(Nk)O(Nk) space and uses a parameter kkk to trade off space and time. We also propose a set of novel and reusable building blocks that compose cuSLINK. These building blocks include highly optimized computational patterns for kkk-NN graph construction, spanning trees, and dendrogram cluster extraction. We show how we used our primitives to implement cuSLINK end-to-end on the GPU, further enabling a wide range of real-world data mining and machine learning applications that were once intractable. In addition to being a primary computational bottleneck in the popular HDBSCAN algorithm, the impact of our end-to-end cuSLINK algorithm spans a large range of important applications, including cluster analysis in social and computer networks, natural language processing, and computer vision. Users can obtain cuSLINK at https://docs.rapids.ai/api/cuml/latest/api/#agglomerative-clustering

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