Personally while I appreciate what PyPy are trying to do, for a lot of real world use cases you can get really impressive performance out of python using the right libraries (see: the entire Python scientific computing stack).
You get impressive performance out of python the same way you get impressive performance out of any dynamic language (with few exceptions): make sure your hot paths are executed by code written in C. The nice thing about PyPy is that it makes your pure Python code a lot faster. For instance, I wrote some log-parsing code in pure Python that got a 2.5x speedup when ran from PyPy.
Add Julia to the list, since it's directly competing with Python on scientific computing, and the other two aren't really that much (maybe Go a little).
If you are talking about scientific computing, sure, compare PyPy, R, and Julia (and probably add cython, numba and company to make a meaningful comparison).
But the OP seems to be interested in web dev where Julia does not deserve the same attention as PyPy, node, and Go.
Julia might be intended for a scientific audience, but there doesn't appear to be any reason why it couldn't address server-side needs. It just needs the right library (like Python did).
By the time Julia hits 1.0, I wouldn't be surprised if someone had developed a decent networking library.
