Published by the Foundation for Open Access Statistics
Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
somoclu: An Efficient Parallel Library for Self-Organizing Maps | Wittek | Journal of Statistical Software
Authors: Peter Wittek, Shi Chao Gao, Ik Soo Lim, Li Zhao
Title: somoclu: An Efficient Parallel Library for Self-Organizing Maps
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.

Page views:: 1614. Submitted: 2014-04-23. Published: 2017-06-09.
Paper: somoclu: An Efficient Parallel Library for Self-Organizing Maps     Download PDF (Downloads: 948)
Supplements:
somoclu-1.7.4.tar.gz: Source package and interfaces Download (Downloads: 57; 1MB)
pypi-somoclu-1.7.4.tar.gz: Python source package from PyPI Download (Downloads: 67; 5MB)
Rsomoclu_1.7.4.tar.gz: R source package Download (Downloads: 52; 13KB)
v78i09-replication.zip: Replication materials Download (Downloads: 37; 108KB)

DOI: 10.18637/jss.v078.i09

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.