ParChain: a framework for parallel hierarchical agglomerative clustering using nearest-neighbor chain
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3489496.3489509.pdf
Description
Published version
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1.49 MB
Format
Adobe PDF
Checksum (MD5)
9eb561a0390b5e4edd8b88bcc2059000
Author(s)
Yu, Shangdi
Wang, Yiqiu
Gu, Yan
Dhulipala, Laxman
Shun, Julian
Date Issued
2021
Journal
Proceedings of the VLDB Endowment
Publisher
VLDB Endowment
Citation
Yu, Shangdi, Wang, Yiqiu, Gu, Yan, Dhulipala, Laxman and Shun, Julian. 2021. "ParChain: a framework for parallel hierarchical agglomerative clustering using nearest-neighbor chain." Proceedings of the VLDB Endowment, 15 (2).
Version
Final published version
Abstract
This paper studies the hierarchical clustering problem, where the goal is to produce a dendrogram that represents clusters at varying scales of a data set. We propose the ParChain framework for designing parallel hierarchical agglomerative clustering (HAC) algorithms, and using the framework we obtain novel parallel algorithms for the complete linkage, average linkage, and Ward's linkage criteria. Compared to most previous parallel HAC algorithms, which require quadratic memory, our new algorithms require only linear memory, and are scalable to large data sets. ParChain is based on our parallelization of the nearest-neighbor chain algorithm, and enables multiple clusters to be merged on every round. We introduce two key optimizations that are critical for efficiency: a range query optimization that reduces the number of distance computations required when finding nearest neighbors of clusters, and a caching optimization that stores a subset of previously computed distances, which are likely to be reused.
Experimentally, we show that our highly-optimized implementations using 48 cores with two-way hyper-threading achieve 5.8--110.1x speedup over state-of-the-art parallel HAC algorithms and achieve 13.75--54.23x self-relative speedup. Compared to state-of-the-art algorithms, our algorithms require up to 237.3x less space. Our algorithms are able to scale to data set sizes with tens of millions of points, which existing algorithms are not able to handle.
Experimentally, we show that our highly-optimized implementations using 48 cores with two-way hyper-threading achieve 5.8--110.1x speedup over state-of-the-art parallel HAC algorithms and achieve 13.75--54.23x self-relative speedup. Compared to state-of-the-art algorithms, our algorithms require up to 237.3x less space. Our algorithms are able to scale to data set sizes with tens of millions of points, which existing algorithms are not able to handle.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Creative Commons Attribution-NonCommercial-NoDerivs License
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DOI of Published Version
https://doi.org/10.14778/3489496.3489509
