Hi Alex, mine might be a minority view/request, but I think RPython is an interesting and useful language in its own right and should be standardized and highlighted. Possibly separated out with a website of its own to emphasize its existence.

When I write Python it is rarely the case that I assign values of different types to the same identifier, or add attributes to a class outside of its definition, or monkey patch a module. In other words most of the time I am not really making use of the dynamic properties of the language. In this scenario Python is a language that saves me "finger typing", and I do not needs its dynamic typing as much. I am more than willing, in these scenarios, to trade some of that unused dynamism for better performance. I think there might be others who are similarly inclined.

The one thing that I miss in RPython is generators. Is there a particular difficulty because of which it was left out ? I assume so, because all the statically typed variants of Python leave it out as well, but I do not have enough background to know what that difficulty might be. The other is Numpy :) but I believe you guys are working on that.

@wbhart A lot of what you ask is answered in some detail in the PyPy blog http://morepypy.blogspot.com/ I wouldnt call PyPy less well known though.

http://stackoverflow.com/questions/6277174/are-generators-su... explains why we don't have generators in RPython

You should put that on the pypy site somewhere.

I'd like to second this request, I would be more than happy to write code in a slightly more restricted way if it meant that I could generate C-fast modules from Python code. In my opinion, RPython is an even bigger deal than PyPy, as it's a very easy way to get C speed with Python syntax.

I have no idea why they haven't given the compiler some love so we can use it for our own projects. It sounds like it would become ubiquitous very, very fast.

Generators probably need to be efficiently inlined to be fast? Maybe this depends on the capabilities of the VM. I think they can also be implemented with continuations which few languages implement due to being a headscrew. I'm just guessing though. I'd be interested in hearing an authoritative answer.

I fully agree re the highlighting and standardizing RPython.

@srean, what do you use RPython for?

Thanks for the concise and extremely helpful answers. I have some followups:

* What is the Jit itself written in?

* You mention that for numeric code PyPy hovers around gcc -O1. What are the other classes of code that you see PyPy as having to deal with? And how would it perform compared to normal C on those?

* Very interesting comments re the benchmark game. Did anyone ever optimise some of these tasks specifically for PyPy?

* Just a clarification re Cython. Yes, I believe it was forked from Pyrex which was mainly for nailing up C code I think. Though since that time my impression is it has become its own language, similar in parts to C/C++ and similar in parts to Python. They appear to have put a fair bit of work into making it fast. It is compiled though (to C), not interpreted. There was some talk about them trying to get it into CPython. But I'm not sure what was meant by that.

One final followup:

* Do you know if there are any plans to develop a new language built on top of the PyPy VM similar to Python but extended in some way? For example would it be feasible to implement a statically typed language with or without type inference on top of the same technology?

By the way, if you google virtual machine you get pages and pages of links to LLVM, JVM, Parrot and Dalvik (go figure) and the occasional reference to a few others. I'm really surprised that the PyPy VM is not better known!

* The JIT is written in RPython.

* Comparing code with C is pretty difficult for all but the easiest 1-1 ports (i.e. numeric stuff), so I don't have good comparison numbers for it.

* Yes, I tried to improve the submissions to the benchmark game, this led down a long path which resulted in the benchmark game only having a single implementation for each language (e.g. tracemonkey is no longer on there, only v8)

* We don't plan on writing a new language, we kind of like Python, but someone absolutely could.

* PyPy doesn't have a VM, we have a toolkit for writing VMs and adding JITs to them. Perhaps these two blog posts will make it more clear: http://morepypy.blogspot.com/2011/04/tutorial-writing-interp... http://morepypy.blogspot.com/2011/04/tutorial-part-2-adding-...

I think I understand now. The PyPy implementation of Python is written using the RPython -> Jit interpreter framework that the PyPy project has. Clever.

Am I right in thinking that the drawback of this method is very long compile times for producing the interpreter?

Yes, compile times are rather long, about 40 minutes for the full interpreter on a recent, fairly good, laptop. On the other hand you don't need to do a compile all that frequently, because all the code is testable by executing directly atop another Python.

So you develop the interpreter by interpreting it on itself before compiling it to itself by itself.

Uh...

You might have some fun reading about the Futamura projections :)

Oh God, that stirs some memories. I'll read it again, thank you.

Cython just has a syntax for annotating your code with types so it can work its magic and make it go fast, although you can add the annotations to a separate file and keep your code in pure Python.

I'm not sure if they're going to use Python 3's annotations to make this cleaner, that might be worth exploring...