Title
ParChain: A Framework for Parallel Hierarchical Agglomerative Clustering using Nearest-Neighbor Chain
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.
Year
DOI
Venue
2021
10.14778/3489496.3489509
PROCEEDINGS OF THE VLDB ENDOWMENT
DocType
Volume
Issue
Journal
15
2
ISSN
Citations 
PageRank 
2150-8097
0
0.34
References 
Authors
28
5
Name
Order
Citations
PageRank
Shangdi Yu132.07
Yiqiu Wang251.79
Yan Gu35710.46
Laxman Dhulipala4786.51
Julian Shun501.01