Hello R masteRs, I am trying to train a neural network whose training set is about 910,000 rows by 5 columns. I am running R on a small cluster of 8 machines (7 slaves) using the SNOW package. Is there a smart way to use all 8 processors to train the neural net? or am I just better off putting all the RAM possible on one machine and run nnet on one processor? I thought about storing each column of the training set on a seperate node and have the master node execute something of the sort: nnet((cbind(clustr[[1]],clustr[[2]],...,clustr[[5]])),targetvector,,5) (where clstr is the name of the contents of the cluster (is diferent from "cl") However I think that when I do that R loads the cbind-ed matrix in the master's node environment and therefore no parallel processing gains are realized. Thanks in advance for your comments and suggestions, Ron.