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Somoclu: An Efficient Parallel Library for Self-Organizing Maps
University of Borås, Faculty of Librarianship, Information, Education and IT.ORCID iD: 0000-0002-1539-8256
2017 (English)In: Journal of Statistical Software, ISSN 1548-7660, E-ISSN 1548-7660, Vol. 78, no 9Article in journal (Refereed) Published
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Abstract [en]

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.

Place, publisher, year, edition, pages
2017. Vol. 78, no 9
Keywords [en]
self-organizing maps, som, esom, emergent self-organizing maps, GPU, CUDA, MPI, parallel implementation, distributed computing, machine learning
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hb:diva-8540DOI: 10.18637/jss.v078.i09OAI: oai:DiVA.org:hb-8540DiVA, id: diva2:894331
Available from: 2016-01-14 Created: 2016-01-14 Last updated: 2018-06-01Bibliographically approved

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Wittek, Peter

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