使用JIT与R的可能缺点？ [英] Possible shortcomings for using JIT with R?

问题描述

我最近发现，可以使用编译器包使用JIT（只是及时）编译R（我总结了我在最近的博客文章）。

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).

我问的问题之一是：

有什么陷阱？

看了一遍后，我可以找到一个可能的问题与JIT的启动时间有关。但是在使用JIT时还有其他问题需要注意吗？

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?

我猜想与R的环境架构有一些限制，但我不能

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?

推荐答案

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 

我还尝试过其他一些例子，例如

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

• li>
  
• 一个只是包装器的函数意味着

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

虽然我并不总是得到加速，但我从来没有经历过显着的减速。

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

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