St Andrews Research Repository

St Andrews University Home
View Item 
  •   St Andrews Research Repository
  • University of St Andrews Research
  • University of St Andrews Research
  • University of St Andrews Research
  • View Item
  •   St Andrews Research Repository
  • University of St Andrews Research
  • University of St Andrews Research
  • University of St Andrews Research
  • View Item
  •   St Andrews Research Repository
  • University of St Andrews Research
  • University of St Andrews Research
  • University of St Andrews Research
  • View Item
  • Login
JavaScript is disabled for your browser. Some features of this site may not work without it.

Large-scale hierarchical k-means for heterogeneous many-core supercomputers

Thumbnail
View/Open
Thomson_2018_LargeScaleKMeans_AAM.pdf (904.3Kb)
Date
11/11/2018
Author
Li, Lideng
Yu, Teng
Zhao, Wenlai
Fu, Haohuan
Wang, Chenyu
Tan, Li
Yang, Guangwen
Thomson, John
Keywords
Supercomputer
Multi/many-core Processors
Clustering
Parallel computing
QA75 Electronic computers. Computer science
NDAS
BDC
Metadata
Show full item record
Altmetrics Handle Statistics
Altmetrics DOI Statistics
Abstract
This paper presents a novel design and implementation of k-means clustering algorithm targeting the Sunway TaihuLight supercomputer. We introduce a multi-level parallel partition approach that not only partitions by dataflow and centroid, but also by dimension. Our multi-level (nkd) approach unlocks the potential of the hierarchical parallelism in the SW26010 heterogeneous many-core processor and the system architecture of the supercomputer. Our 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, significantly improving the capability of k-means over previous approaches. The evaluation shows our implementation achieves performance of less than 18 seconds per iteration for a large-scale clustering case with 196,608 data dimensions and 2,000 centroids by applying 4,096 nodes (1,064,496 cores) in parallel, making k-means a more feasible solution for complex scenarios.
Citation
Li , L , Yu , T , Zhao , W , Fu , H , Wang , C , Tan , L , Yang , G & Thomson , J 2018 , Large-scale hierarchical k-means for heterogeneous many-core supercomputers . in Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC '18) . IEEE Press , Piscataway , The International Conference for High Performance Computing, Networking, Storage, and Analysis , Dallas , United States , 11/11/18 . https://doi.org/10.5555/3291656.3291674
 
conference
 
Publication
Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC '18)
DOI
https://doi.org/10.5555/3291656.3291674
Type
Conference item
Rights
Copyright © 2018 IEEE Press. This work has been made available online in accordance with the publisher's policies. This is the author created accepted version manuscript following peer review and as such may differ slightly from the final published version. The final published version of this work is available at https://dl.acm.org/citation.cfm?id=3291674
Description
Funding: J.Thomson and T.Yu are supported by the EPSRC grants ”Discovery” EP/P020631/1, ”ABC: Adaptive Brokerage for the Cloud” EP/R010528/1, and EU Horizon 2020 grant Team-Play: ”Time, Energy and security Analysis for Multi/Many-core heterogenous PLAtforms” (ICT-779882, https://teamplay- h2020.eu)
Collections
  • University of St Andrews Research
URL
https://dl.acm.org/citation.cfm?id=3291674
URI
http://hdl.handle.net/10023/16441

Items in the St Andrews Research Repository are protected by copyright, with all rights reserved, unless otherwise indicated.

Advanced Search

Browse

All of RepositoryCommunities & CollectionsBy Issue DateNamesTitlesSubjectsClassificationTypeFunderThis CollectionBy Issue DateNamesTitlesSubjectsClassificationTypeFunder

My Account

Login

Open Access

To find out how you can benefit from open access to research, see our library web pages and Open Access blog. For open access help contact: openaccess@st-andrews.ac.uk.

Accessibility

Read our Accessibility statement.

How to submit research papers

The full text of research papers can be submitted to the repository via Pure, the University's research information system. For help see our guide: How to deposit in Pure.

Electronic thesis deposit

Help with deposit.

Repository help

For repository help contact: Digital-Repository@st-andrews.ac.uk.

Give Feedback

Cookie policy

This site may use cookies. Please see Terms and Conditions.

Usage statistics

COUNTER-compliant statistics on downloads from the repository are available from the IRUS-UK Service. Contact us for information.

© University of St Andrews Library

University of St Andrews is a charity registered in Scotland, No SC013532.

  • Facebook
  • Twitter