Analysing python's performance under PyPy
The traditional model of analysing performance of Python programs has been "run the profiler, find your bottlenecks, optimize them or move them to C". I personally find this approach grossly insufficient in many cases, especially in the context of PyPy. Particular problems are: In many large applications, the profile is flat: PyPy's own translation toolchain, Twisted or any modern web servers are
PyPy's future directions
The PyPy project was long criticised for being insufficiently transparent about the direction of its development. This changed drastically with the introduction of the PyPy blog, Twitter stream, etc., but I think there is still a gap between the achievements reported in the blog and our ongoing plans. This post is an attempt to bridge that gap. Note, however, that it is not a roadmap -- merely a p
How fast PyPy really is?
Martijn Faassen used to ask "how fast is PyPy", so we added the --faassen option to compile toolchain to enable all the optimizations we had. Back then, we didn't have a JIT and PyPy's interpreter was quite a bit slower than CPython's (it still is), but the situation has changed quite drastically since the introduction of the JIT. We even had to remove --faassen command line option! So, let's repe
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