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Question

I am running random forest in R in parallel

library(doMC)
registerDoMC()
x <- matrix(runif(500), 100)
y <- gl(2, 50)

Parallel execution (took 73 sec)

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

Sequential execution (took 82 sec)

rf <- foreach(ntree=rep(25000, 6), .combine=combine) %do%
randomForest(x, y, ntree=ntree) 

In parallel execution, the tree generation is pretty quick like 3-7 sec, but the rest of the time is consumed in combining the results (combine option). So, its only worth to run parallel execution is the number of trees are really high. Is there any way I can tweak "combine" option to avoid any calculation at each node which I dont need and make it more faster

PS. Above is just an example of data. In real I have some 100 thousands features for some 100 observations.

Answer

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.