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