Can I tangentially point out without much connection to this benchmark more than any of the others recently, that one of the great things about PyPy is that if you have a thing and you run it on PyPy, you can usually pop in the IRC channel and often get even more tips on how to tune it to be even faster?
There are the simple tips like "write everything in Python where possible, don't use C extensions" like the OP noticed, but even after you've made the decision on using PyPy there are often specific performance characteristics of the PyPy implementation that can be really helpful to keep in mind, and it's a great resource to try and take advantage of (human interaction with PyPy developers like fijal who care about making things fast).
Can you give an example of a tip that would speed a particular program up? Does PyPy have a lot of knobs to tune?
CPython doesn't seem to have that many knobs... You can tune some settings in GC, and maybe the GIL acquiring interval, but it doesn't seem to produce speedups in most programs.
PyPy does have a few knobs to tune, but they're not very often used (they're nicely documented, however mostly in the source code, I'll try to pull say GC parameters more prominently in the docs).
However, the way you write python code can be tuned a lot. This is actually, at least to an extend, our failure. JIT can make things really fast, but can get confused in places. We're trying to eliminate them as we go, but it can't be always done.
Generators vs list comprehensions. Not using sys._getframe, sys.exc_info, etc. not relying too much on dynamism like passing very general kwargs and *args, the list goes on.
Jitviewer can be a lot of help, but it's a bit buggy and too low level to be seriously recommended.
"As of now (Feb 2011) generators are usually slower than corresponding code not using generators. Same goes for generator expressions. For most cases using list comprehension is faster."
There are the simple tips like "write everything in Python where possible, don't use C extensions" like the OP noticed, but even after you've made the decision on using PyPy there are often specific performance characteristics of the PyPy implementation that can be really helpful to keep in mind, and it's a great resource to try and take advantage of (human interaction with PyPy developers like fijal who care about making things fast).