Large-Scale Automatic K-Means Clustering for Heterogeneous Many-Core Supercomputer
- Submitting institution
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University of Dundee
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 48640883
- Type
- D - Journal article
- DOI
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10.1109/TPDS.2019.2955467
- Title of journal
- IEEE Transactions on Parallel and Distributed Systems
- Article number
- -
- First page
- 997
- Volume
- 31
- Issue
- 5
- ISSN
- 1045-9219
- Open access status
- Compliant
- Month of publication
- November
- Year of publication
- 2019
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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8
- Research group(s)
-
-
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper presents a novel implementation of the automatic k-means clustering algorithm which is used widely in, for example, bioinformatics, image analysis, and information retrieval. A key contribution of this work is the hierarchical data partitioning scheme, applicable to all systems that have hierarchical organisation of memory, which includes all modern supercomputers. Evaluation on the Sunway TaihuLight, the 4th fastest supercomputer in the world (www.top500.org), shows that this implementation can process problems with orders of magnitude more dimensions and clusters than the state-of-the-art solutions.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -