Making R run Fast - Jan Vitek

Another talk on the difficulty of optimising R is this fantastic one by Jan Vitek: link to this video Also, my favourite R trick: foo <- function() {rm(foo, envir=environment(foo))}

Perhaps the best way to understand what makes Julia fast is to watch these two videos about Python and R and what makes them so hard to optimize: link link to this video Take everything mentioned in these videos that make Python and R really hard to optimize and don't do those things :D

I recommend watching this video - Making R run fast link to this video It's a little disappointing, because the conclusion is that R will probably never "run fast", but very interesting nonetheless.

…and TensorFlow XLA, now PyTorch v2's JIT, etc.). What has been found over the last decade is that a large part of that is due to the design of the languages. Jan Vitek for example has a great talk which describes how difficult it is to write a JIT compiler for R due to certain design choices in the language ( link , or the more detailed version link to this video ). There are certain language constructs that void lots of optimizations which have to then be worked around, which is why Python JITs choose subsets of the language to avoid specific parts that are not easy to optimize or not possible to optimize. This is why each take a domain-specific subset, a different subset of the language for numba…