[pypy-dev] PyPy 1.8 released

[gelin yan]

On Fri, Feb 10, 2012 at 5:44 PM, Maciej Fijalkowski <fijall at gmail.com>wrote:

> ============================
> PyPy 1.8 - business as usual
> ============================
>
> We're pleased to announce the 1.8 release of PyPy. As habitual this
> release brings a lot of bugfixes, together with performance and memory
> improvements over the 1.7 release. The main highlight of the release
> is the introduction of `list strategies`_ which makes homogenous lists
> more efficient both in terms of performance and memory. This release
> also upgrades us from Python 2.7.1 compatibility to 2.7.2. Otherwise
> it's "business as usual" in the sense that performance improved
> roughly 10% on average since the previous release.
>
> you can download the PyPy 1.8 release here:
>
>    http://pypy.org/download.html
>
> .. _`list strategies`:
>
> http://morepypy.blogspot.com/2011/10/more-compact-lists-with-list-strategies.html
>
> What is PyPy?
> =============
>
> PyPy is a very compliant Python interpreter, almost a drop-in replacement
> for
> CPython 2.7. It's fast (`pypy 1.8 and cpython 2.7.1`_ performance
> comparison)
> due to its integrated tracing JIT compiler.
>
> This release supports x86 machines running Linux 32/64, Mac OS X 32/64 or
> Windows 32. Windows 64 work has been stalled, we would welcome a volunteer
> to handle that.
>
> .. _`pypy 1.8 and cpython 2.7.1`: http://speed.pypy.org
>
>
> Highlights
> ==========
>
> * List strategies. Now lists that contain only ints or only floats should
>  be as efficient as storing them in a binary-packed array. It also improves
>  the JIT performance in places that use such lists. There are also special
>  strategies for unicode and string lists.
>
> * As usual, numerous performance improvements. There are many examples
>  of python constructs that now should be faster; too many to list them.
>
> * Bugfixes and compatibility fixes with CPython.
>
> * Windows fixes.
>
> * NumPy effort progress; for the exact list of things that have been done,
>  consult the `numpy status page`_. A tentative list of things that has
>  been done:
>
>  * multi dimensional arrays
>
>  * various sizes of dtypes
>
>  * a lot of ufuncs
>
>  * a lot of other minor changes
>
>  Right now the `numpy` module is available under both `numpy` and `numpypy`
>  names. However, because it's incomplete, you have to `import numpypy`
> first
>  before doing any imports from `numpy`.
>
> * New JIT hooks that allow you to hook into the JIT process from your
> python
>  program. There is a `brief overview`_ of what they offer.
>
> * Standard library upgrade from 2.7.1 to 2.7.2.
>
> Ongoing work
> ============
>
> As usual, there is quite a bit of ongoing work that either didn't make it
> to
> the release or is not ready yet. Highlights include:
>
> * Non-x86 backends for the JIT: ARMv7 (almost ready) and PPC64 (in
> progress)
>
> * Specialized type instances - allocate instances as efficient as C
> structs,
>  including type specialization
>
> * More numpy work
>
> * Since the last release there was a significant breakthrough in PyPy's
>  fundraising. We now have enough funds to work on first stages of
> `numpypy`_
>  and `py3k`_. We would like to thank again to everyone who donated.
>
> * It's also probably worth noting, we're considering donations for the
>  Software Transactional Memory project. You can read more about `our
> plans`_
>
> Cheers,
> The PyPy Team
>
> .. _`brief overview`: http://doc.pypy.org/en/latest/jit-hooks.html
> .. _`numpy status page`: http://buildbot.pypy.org/numpy-status/latest.html
> .. _`numpy status update blog report`:
> http://morepypy.blogspot.com/2012/01/numpypy-status-update.html
> .. _`numpypy`: http://pypy.org/numpydonate.html
> .. _`py3k`: http://pypy.org/py3donate.html
> .. _`our plans`:
> http://morepypy.blogspot.com/2012/01/transactional-memory-ii.html
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>


 a great job...I will give a try tonight...
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