TY - JOUR
T1 - Somoclu: An Efficient Parallel Library for Self-Organizing Maps
AU - Wittek, Peter
AU - Gao, Shi Chao
AU - Lim, Ik Soo
AU - Zhao, Li
N1 - European Commission Seventh Frame-work Programme under Grant Agreement Number FP7-601138 PERICLES
and by the AWS in Education Machine Learning Grant award
PY - 2017/6/9
Y1 - 2017/6/9
N2 - 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.
AB - 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.
UR - https://www.jstatsoft.org/index.php/jss/article/downloadSuppFile/v078i09/somoclu-1.7.4.tar.gz
U2 - 10.18637/jss.v078.i09
DO - 10.18637/jss.v078.i09
M3 - Article
SN - 1548-7660
VL - 78
SP - 1
EP - 21
JO - Journal of Statistical Software
JF - Journal of Statistical Software
IS - 9
ER -