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.

Keywords

• SOM
• ESOM
• distributed computing
• parallel computing
• multicore
• GPU
• C++
• CUDA
• Python
• R
• MATLAB