Abstract
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 autoclustering 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 language | English |
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Pages (from-to) | 997-1008 |
Number of pages | 12 |
Journal | IEEE Transactions on Parallel and Distributed Systems |
Volume | 31 |
Issue number | 5 |
Early online date | 25 Nov 2019 |
DOIs | |
Publication status | Published - 1 May 2020 |
Keywords
- AutoML
- Supercomputer
- clustering
- data partitioning
- heterogeneous many-core processor
- scheduling
ASJC Scopus subject areas
- Signal Processing
- Hardware and Architecture
- Computational Theory and Mathematics