On Mon, Nov 21, 2011 at 12:35 +0200, Maciej Fijalkowski wrote:
> * PyPy now comes with stackless features enabled by default. However,
>   any loop using stackless features will interrupt the JIT for now, so no real
>   performance improvement for stackless-based programs. Contact pypy-dev for
>   info how to help on removing this restriction.

Yes, please, could you talk a bit more explicitely about what is involved
and what works/integrated and what doesn't?

best,
holger

> * NumPy effort in PyPy was renamed numpypy. In order to try using it, simply
>   write::
> 
>     import numpypy as numpy
> 
>   at the beginning of your program. There is a huge progress on numpy in PyPy
>   since 1.6, the main feature being implementation of dtypes.
> 
> * JSON encoder (but not decoder) has been replaced with a new one. This one
>   is written in pure Python, but is known to outperform CPython's C extension
>   up to **2 times** in some cases. It's about **20 times** faster than
>   the one that we had in 1.6.
> 
> * The memory footprint of some of our RPython modules has been drastically
>   improved. This should impact any applications using for example 
> cryptography,
>   like tornado.
> 
> * There was some progress in exposing even more CPython C API via cpyext.
> 
> Things that didn't make it, expect in 1.8 soon
> ==============================================
> 
> There is an ongoing work, which while didn't make it to the release, is
> probably worth mentioning here. This is what you should probably expect in
> 1.8 some time soon:
> 
> * 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
> 
> * NumPy effort is progressing forward, with multi-dimensional arrays coming
>   soon.
> 
> * There are two brand new JIT assembler backends, notably for the PowerPC and
>   ARM processors.
> 
> Fundraising
> ===========
> 
> It's maybe worth mentioning that we're running fundraising campaigns for
> NumPy effort in PyPy and for Python 3 in PyPy. In case you want to see any
> of those happen faster, we urge you to donate to `numpy proposal`_ or
> `py3k proposal`_. In case you want PyPy to progress, but you trust us with
> the general direction, you can always donate to the `general pot`_.
> 
> .. _`numpy proposal`: http://pypy.org/numpydonate.html
> .. _`py3k proposal`: http://pypy.org/py3donate.html
> .. _`general pot`: http://pypy.org
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> 
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