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somoclu: An Efficient Parallel Library for Self-Organizing Maps

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

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.

Suggested Citation

  • Wittek, Peter & Gao, Shi Chao & Lim, Ik Soo & Zhao, Li, 2017. "somoclu: An Efficient Parallel Library for Self-Organizing Maps," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 78(i09).
    • Handle: RePEc:jss:jstsof:v:078:i09
    DOI: http://hdl.handle.net/10.18637/jss.v078.i09
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    References listed on IDEAS

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    1. Theußl, Stefan & Feinerer, Ingo & Hornik, Kurt, 2012. "A tm Plug-In for Distributed Text Mining in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 51(i05).
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