title: In R, is there any way to share a variable between difference processes of R in the same machine?
body: <p>My problem is that I have a large model, which is slow to load to memory. To test it on many samples, I need to run some C program to generating input features for model, then run R script to predict. It takes too much time to load the model every time.</p>
  
  <p>So I am wondering </p>
  
  <p>1) if there is some method to keep the model ( a variable in R) in the memory. </p>
  
  <p>or</p>
  
  <p>2) Can I run a separative process of R as a dedicated server, then all the prediction processes of R can access the variable in the server on the same machine. </p>
  
  <p>The model is never changed during for all the prediction. It is a randomForest model stored in a .rdata file, which has ~500MB. Loading this model is slow.</p>
  
  <p>I know that I can use parallel R (snow, doPar, etc) to perform prediction in parallel, however, this is not what I want, since it require me to change the data flow I used.</p>
  
  <p>Thanks a lot.</p>