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.
@article{arxiv.1305.1422,
title = {Somoclu: An Efficient Parallel Library for Self-Organizing Maps},
author = {Peter Wittek and Shi Chao Gao and Ik Soo Lim and Li Zhao},
journal= {arXiv preprint arXiv:1305.1422},
year = {2017}
}
Comments
26 pages, 9 figures. The code is available at https://peterwittek.github.io/somoclu/