PyPy 1.8 released
fijall at gmail.com
Fri Feb 10 10:45:01 CET 2012
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:
.. _`list strategies`:
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
* 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
* 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.
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`_
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`:
.. _`numpypy`: http://pypy.org/numpydonate.html
.. _`py3k`: http://pypy.org/py3donate.html
.. _`our plans`:
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