That 3x speed up is about the same that I have seen with my code. I'm currently writing a database cache simulator to try different algorithms with it, and if I want to have anywhere near realistic results I have to use realistic access traces.<p>Tried it today with a tpc-c trace which has about 500 million accesses. The result: CPython would have run for about 90 minutes (I stopped it after 30 minutes, and began to look for a speedier possibility), PyPy only took 22 minutes.
This is really really great to see: pypy is such an interesting project, and it's really encouraging to see it make so much ground.<p>It's interesting that Guido deliberately didn't go for a full re write for python 3, but this project which is a full rewrite in a whole different language has provided a faster implementation with less developers!
Can't wait to have a version of PyPy that supports numpy!<p>Since the benefits have been proven, I can't help but wonder why there aren't more people working on this...
Can anyone comment on startup latency and performance early in a run?<p>One of my use cases for Python is relatively small, short-running scripts, and JIT engines often take a fair amount of runtime before all the optimizations kick in. So I wonder how PyPy does at startup time and whether it's able to leverage its execution speed prowess over brief runtimes - is it still a net performance win in the end, or at least not-worse-than-CPython?