# Parallelizing WGCNA k-means clustering and merging smaller clusters

WGCNA carefully takes advantage of server parallelization in several functions, and I can switch this on with:

enableWGCNAThreads(nThreads=28)


That speeds up some functions significantly, but the two bottleneck functions in the pipeline (in my experience) are part of the "Projective k-means" operation, primarily "..k-means clustering.." and "..merging smaller clusters..".

Unfortunately, parallelization does not work for those two functions.

Am I missing something? How can I parallelize these functions? Otherwise I'm wasting HPC resources and cluster time.

For reference, I'm working on a 28-processor HPC node with 96GB RAM, and I'm trying to run analysis on a 15,000-square correlation matrix.