somoclu: An Efficient Parallel Library for Self-Organizing Maps

Peter Wittek, Shi Chao Gao, Ik Soo Lim and Li Zhao

Journal of Statistical Software, 2017, vol. 078, issue i09

Abstract: somoclu is a massively parallel tool for training self-organizing maps on large data sets written in C++. It builds on OpenMP for multicore execution, and on MPI for distributing the workload across the nodes in a cluster. It is also able to boost training by using CUDA if graphics processing units are available. A sparse kernel is included, which is useful for high-dimensional but sparse data, such as the vector spaces common in text mining workflows. Python, R and MATLAB interfaces facilitate interactive use. Apart from fast execution, memory use is highly optimized, enabling training large emergent maps even on a single computer.

Date: 2017-06-09
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.jstatsoft.org/index.php/jss/article/view/v078i09/v78i09.pdf
https://www.jstatsoft.org/index.php/jss/article/do ... somoclu-1.7.4.tar.gz
https://www.jstatsoft.org/index.php/jss/article/do ... somoclu-1.7.4.tar.gz
https://www.jstatsoft.org/index.php/jss/article/do ... somoclu_1.7.4.tar.gz
https://www.jstatsoft.org/index.php/jss/article/do ... 8i09-replication.zip

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:jss:jstsof:v:078:i09

DOI: 10.18637/jss.v078.i09

Access Statistics for this article

Journal of Statistical Software is currently edited by Bettina Grün, Edzer Pebesma and Achim Zeileis

More articles in Journal of Statistical Software from Foundation for Open Access Statistics
Bibliographic data for series maintained by Christopher F. Baum ().