somoclu: An Efficient Parallel Library for Self-Organizing Maps

Wittek, Peter

doi:10.18637/jss.v078.i09

somoclu: An Efficient Parallel Library for Self-Organizing Maps

2017-01-01

56

OA

AI

DOI：10.18637/jss.v078.i09

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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.

Keyword:

SOM

ESOM

distributed computing

parallel computing

multicore

GPU

C plus

CUDA

Python

R

MATLAB

somoclu: An Efficient Parallel Library for Self-Organizing Maps

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