

PyPy 1.7 released: widening the sweet spot - kingkilr
http://morepypy.blogspot.com/2011/11/pypy-17-widening-sweet-spot.html

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binarycrusader
PyPy performance is good, but the project is still not very mature. It's not
quite yet ready as a drop-in replacement.

In particular, the build process is really painful (3+ hours on a high-end
Xeon workstation) and quite immature.

I was particularly annoyed to discover that the build process isn't
incremental -- if it fails for any reason, you get to start all over again.

The CPython extension support is also still very experimental (that is, if you
have any Python extension modules written in C, be prepared for a lot of fun
getting them to work).

PyPy also doesn't support anywhere near the number of OS platforms that Python
does natively. It's very much a Linux, Mac, or Windows thing only. And it's
heavily focused on x86 if you want to use the JIT version.

~~~
dripton
3 hours? You're doing something wrong. I just built PyPy in 49 minutes on a
$99 3 GHz Phenom II.

~~~
obtu
You probably have more RAM?

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mattdeboard
It is hard to tell from some of the release statements when they mean a
particular thing is an improvement over CPython or over previous performance
by PyPy, e.g.:

> _Specialized list implementation. There is a branch that implements lists of
> integers/floats/strings as compactly as array.array. This should drastically
> improve performance/memory impact of some applications_

Is this improvement over CPython or over previous implementation in PyPy?
There are several such comments.

~~~
vladev
Currently, PyPy is faster than CPython at pretty much anything (except when C
is involved, for example, but they are working on that, too). You can see
their benchmarks and comparisons at <http://speed.pypy.org/>.

What I think they mean by those statements is that this will improve
performance over the previous version of PyPy - therefore - improve over
CPython as well.

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YoukaiCountry
I took the plunge and switched over to pypy with version 1.6. Besides the
occasional minor headache, most things work as expected and the huge speed-up
makes it more than worth it.

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levesque
I, for one, will certainly consider switching to pypy when they have a stable,
usable implementation of numpy :)

~~~
wisty
It's likely to take a while. See Travis's post:
[http://technicaldiscovery.blogspot.com/2011/10/thoughts-
on-p...](http://technicaldiscovery.blogspot.com/2011/10/thoughts-on-porting-
numpy-to-pypy.html)

There are two main problems he sees - numpy is a moving target (as it
continues to evolve), and the C-API of numpy is one of its most useful
features (as lots of numpy extensions like Scipy and matplotlib may require
it).

~~~
fijal
As you might know, we disagree with Travis when it comes to timelines. C API
is not really useful without CPython C API and we're trying hard to make pypy
interoperate with C. Look for f2pypy for an example.

