PyPy gets faster

Not that I'm a big fan of Jython, but including startup time in a benchmark is only useful for very short running command line tools. It says nothing about the speed of the JIT or the code that's being tested.

Has anyone done any recent memory benchmarks with PyPy?<p>I'd like to see magnitude of the memory trade-off for using a JIT compiler. As a web developer my programs are mostly IO-bound, not CPU-bound. I'm also bootstrapping and trying to squeeze as much as I can out of my 512MB linode.

Competition in the python-vm space is <i>fantastic</i> and I'm actually really, really happy to see PyPy maturing.

That's really nice for PyPy. Although Eulers are mainly numerical tests. Most of the work done in many programmers "real world" are work on strings, which CPython is very good at since all its strings libs are implemented in C. So i'd like to see a comparison with a better benchmark i guess :)

I'd like to see IronPython included in the comparison. (do they handle 2.7?)

Just to be clear, what are the specified numbers? execution time in seconds?

PyPy needs epoll!

Years and years later and they are finally able to (very slightly) beat psycho. Sigh.