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Large-scale automatic k-means clustering for heterogeneous many-core supercomputer

Research output: Contribution to journalArticlepeer-review


Teng Yu, Wenlai Zhao, Pan Liu, Vladimir Janjic, Xiaohan Yan, Shicai Wang, Haohuan Fu, Guangwen Yang, John Donald Thomson

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This article presents an automatic k-means clustering solution targeting the Sunway TaihuLight supercomputer. We first introduce a multilevel parallel partition approach that not only partitions by dataflow and centroid, but also by dimension, which unlocks the potential of the hierarchical parallelism in the heterogeneous many-core processor and the system architecture of the supercomputer. The parallel design is able to process large-scale clustering problems with up to 196,608 dimensions and over 160,000 targeting centroids, while maintaining high performance and high scalability. Furthermore, we propose an automatic hyper-parameter determination process for k-means clustering, by automatically generating and executing the clustering tasks with a set of candidate hyper-parameter, and then determining the optimal hyper-parameter using a proposed evaluation method. The proposed auto-clustering solution can not only achieve high performance and scalability for problems with massive high-dimensional data, but also support clustering without sufficient prior knowledge for the number of targeted clusters, which can potentially increase the scope of k-means algorithm to new application areas.


Original languageEnglish
Pages (from-to)997-1008
Number of pages12
JournalIEEE Transactions on Parallel and Distributed Systems
Issue number5
Early online date27 Nov 2019
Publication statusPublished - May 2020

    Research areas

  • Supercomputer, Heterogeneous many-core processor, Data partitioning, Clustering, Scheduling, AutoML

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