[SOLVED] Possible shortcomings for using JIT with R?

Possible shortcomings for using JIT with R?

I recently discovered that one can use JIT (just in time) compilation with R using the compiler package (I summarizes my findings on this topic in a recent blog post).

One of the questions I was asked is:

Is there any pitfall? it sounds too good to be true, just put one line of code and that's it.

After looking around I could find one possible issue having to do with the "start up" time for the JIT. But is there any other issue to be careful about when using JIT?

I guess that there will be some limitation having to do with R's environments architecture, but I can not think of a simple illustration of the problem off the top of my head, any suggestions or red flags will be of great help?

Solution

The rpart example given above, no longer seems to be an issue:

library("rpart")
fo = function() {
  for(i in 1:500){
    rpart(Kyphosis ~ Age + Number + Start, data=kyphosis)
  }
}    system.time(fo())
#   user  system elapsed 
#  1.212   0.000   1.206 
compiler::enableJIT(3)
# [1] 3
system.time(fo())
#   user  system elapsed 
#  1.212   0.000   1.210

I've also tried a number of other examples, such as

growing a vector;
A function that's just a wrapper around mean

While I don't always get a speed-up, I've never experience a significant slow-down.

R> sessionInfo()
R version 3.3.0 (2016-05-03)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04 LTS