cuSLINK: Single-linkage Agglomerative Clustering on the GPU
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 space and uses a parameter 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 -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
Cite
@article{arxiv.2306.16354,
title = {cuSLINK: Single-linkage Agglomerative Clustering on the GPU},
author = {Corey J. Nolet and Divye Gala and Alex Fender and Mahesh Doijade and Joe Eaton and Edward Raff and John Zedlewski and Brad Rees and Tim Oates},
journal= {arXiv preprint arXiv:2306.16354},
year = {2023}
}
Comments
To appear in ECML PKDD 2023 by Springer Nature