parallel execution of random forest in R

Are you aware that the caret package can do a lot of the hand-holding for parallel runs (as well as data prep, summaries, ...) for you?

Ultimately, of course, if there are some costly operations left in the random forest computation itself, there is little you can do as Andy spent quite a few years on improving it. I would expect few to no low-hanging fruits to be around for the picking...

I wonder if the parallelRandomForest code would be helpful to you?

According to the author it ran about 6 times faster on his data set with 16 times less memory consumption.

SPRINT also has a parallel implementation here.

H20 package can be used to solve your problem.

According to H20 documentation page H2O is "the open source math engine for big data that computes parallel distributed machine learning algorithms such as generalized linear models, gradient boosting machines, random forests, and neural networks (deep learning) within various cluster environments."

Random Forest implementation using H2O:

https://www.analyticsvidhya.com/blog/2016/05/h2o-data-table-build-models-large-data-sets/

Setting .multicombine to TRUE can make a significant difference:

rf <- foreach(ntree=rep(25000, 6), .combine=randomForest::combine,
              .multicombine=TRUE, .packages='randomForest') %dopar% {
    randomForest(x, y, ntree=ntree)
}

This causes combine to be called once rather than five times. On my desktop machine, this runs in 8 seconds rather than 19 seconds.