if you can do X in C/C++ with performance Y, can you do X in Ruby/Python with performance close to Y?

Yes. Take, as an example, PyPy. It is a collection of Python code that performs close to C in doing interpretation (not all that close, but not all that far away either). It does this by performing full-program analysis on the source code to assign each variable a static type (see the Annotator and Rtyper docs for details), and then, once armed with the same type information you give C, it can perform the same sorts of optimizations. At least in theory.

The tradeoff of course is that only a subset of Python code is accepted, or rather, only a subset of Python code can do well: the subset that can be analyzed and given static types.

If you restrict Python enough, optimizers can be built that can take advantage of the restricted subset and compile it to efficient code. This is not really an interesting benefit, in fact, it's well known. But the whole point of using Python (or Ruby) in the first place was that we wanted to use interesting features that perhaps don't analyze well and result in good performance! So the interesting question is actually...

Additionally, will JIT compilers, such as PyPy/Rubinius ever match C/C++ in performance?

Nah.

By which I mean: sure, maybe as code runs accumulate you can get enough typing information and enough hotspots to compile all of the code all the way down to machine code. And maybe we can get this to perform better than C for some code. I don't think that's hugely controversial. But it still has to "warm up", and performance is still a bit less predictable, and it won't be as good as C or C++ for certain tasks that require consistently and predictably high performance.

The existing performance data for Java, which has both more type information than Python or Ruby, and a better-developed JIT compiler than Python or Ruby, still doesn't match up to C/C++. It is, however, in the same ballpark.

if you can do X in C/C++ with performance Y, can you do X in Ruby/Python with performance close to Y?

Yes. Take, as an example, PyPy. It is a collection of Python code that performs close to C in doing interpretation (not all that close, but not all that far away either). It does this by performing full-program analysis on the source code to assign each variable a static type (see the Annotator and Rtyper docs for details), and then, once armed with the same type information you give C, it can perform the same sorts of optimizations. At least in theory.

The tradeoff of course is that only a subset of Python code is accepted by RPython, and in general, even if that restriction is lifted, only a subset of Python code can do well: the subset that can be analyzed and given static types.

If you restrict Python enough, optimizers can be built that can take advantage of the restricted subset and compile it to efficient code. This is not really an interesting benefit, in fact, it's well known. But the whole point of using Python (or Ruby) in the first place was that we wanted to use interesting features that perhaps don't analyze well and result in good performance! So the interesting question is actually...

Additionally, will JIT compilers, such as PyPy/Rubinius ever match C/C++ in performance?

Nah.

By which I mean: sure, maybe as code runs accumulate you can get enough typing information and enough hotspots to compile all of the code all the way down to machine code. And maybe we can get this to perform better than C for some code. I don't think that's hugely controversial. But it still has to "warm up", and performance is still a bit less predictable, and it won't be as good as C or C++ for certain tasks that require consistently and predictably high performance.

The existing performance data for Java, which has both more type information than Python or Ruby, and a better-developed JIT compiler than Python or Ruby, still doesn't match up to C/C++. It is, however, in the same ballpark.