Most popular languages attract many compiler implementations. I’m not saying that any of these implementations have more than a handful of users, that they implement the full language (a full implementation is not common), or that they fulfil any need other than their implementers desire to build something.
A commonly heard reason for the lack of production R compilers is that it is not worth the time and effort, because most of an R program’s time is spent in the library code which is written in a compiled language (e.g., C or Fortran). The fact that it is probably not worth the time and effort has not stopped people writing compilers for other languages, but then I think that the kind of people who use R tend not to be the kind of people who want to spend their time writing compilers. On the whole, they are the kind of people who are into statistics and data analysis.
Is it true that that most R programs spend most of their time executing library code? It’s certainly true for me. But I have noticed that a lot of the library functions executed by my code are written in R. Also, if somebody uses R for all their programming needs (it might be the only language they know), then their code might not be heavily library dependent.
I was surprised to read about Tierney’s byte code compiler, because his implementation is how I thought the R-core’s existing implementation worked (it does now). The internals of R is based on 1980s textbook functional techniques, and like many book implementations of the day, performance is dependent on the escape hatch of compiled code. R’s implementers wisely spent their time addressing user concerns, which revolved around statistics and visual presentation, i.e., not internal implementation technicalities.
Building an R compiler is easy, the much harder and time-consuming part is the runtime system.
Threaded code is a quick and simple approach to compiler implementation. R source gets mapped to a sequence of C function calls, with these functions proving a wrapper to library functions implementing the appropriate basic functionality, e.g., add two vectors. This approach has been the subject of at least one Master’s thesis. Thesis implementations rarely reach production use because those involved significantly underestimate the work that remains to be done, which is usually a lot more than the original implementation.
A simple threaded code approach provides a base for subsequent optimization, with the base having a similar performance to an interpreter. Optimizing requires figuring out details of the operations performed and replacing generic function calls with ones designed to be fast for specific cases, or even better replacing calls with inline code, e.g., adding short vectors of integers. There is a lot of existing work for scripting languages and a few PhD thesis researching R (e.g., Wang). The key technique is static analysis of R source.
Jan Vitek is running what appears to be the most active R compiler research group, at the moment e.g., the Å˜ project. Research can be good for uncovering language usage and trying out different techniques, but it is not intended to produce industry strength code. Lots of the fancy optimizations in early versions of the gcc C compiler started life as a PhD thesis, with the respective individual sometimes going on to spend a few years creating a production quality version for the released compiler.
The essential ingredient for building a production compiler is persistence. There are an awful lot of details that need to be sorted out (this is why research project code does not directly translate to production code, they ignore ‘minor’ details in order to concentrate on the ‘interesting’ research problem). Is there a small group of people currently beavering away on a production quality compiler for R? If there is, I can understand being discrete, on long-term projects it can be very annoying to have people regularly asking when the software is going to be released.
To have a life, once released, a production compiler needs to attract users, who are often loyal to their current compiler (because they know that their code works for this compiler); there needs to be a substantial benefit to entice people to switch. The benefit of compiling R to machine code, rather than interpreting, is performance. What performance improvement is needed to attract a viable community of users (there is always a tiny subset of users who will pay lots for even small performance improvements)?
My R code is rarely cpu bound, so I am not in the target audience, no matter what the speed-up. I don’t have any insight in the performance problems experienced by the R community, and have no idea whether a factor of two, five, ten or more would be enough.