The PyPy 0.6 release
The PyPy Development Team is happy to announce the first public release of PyPy after two years of spare-time and half a year of EU funded development. The 0.6 release is eminently a preview release.
What it is and where to start
Getting started: getting-started.html
PyPy Documentation: index.html
PyPy Homepage: http://pypy.org
PyPy is a MIT-licensed reimplementation of Python written in Python itself. The long term goals are an implementation that is flexible and easy to experiment with and retarget to different platforms (also non-C ones) and such that high performance can be achieved through high-level implementations of dynamic optimization techniques.
The interpreter and object model implementations shipped with 0.6 can be run on top of CPython and implement the core language features of Python as of CPython 2.3. PyPy passes around 90% of the Python language regression tests that do not depend deeply on C-extensions. Some of that functionality is still made available by PyPy piggy-backing on the host CPython interpreter. Double interpretation and abstractions in the code-base make it so that PyPy running on CPython is quite slow (around 2000x slower than CPython ), this is expected.
This release is intended for people that want to look and get a feel into what we are doing, playing with interpreter and perusing the codebase. Possibly to join in the fun and efforts.
Interesting bits and highlights
The release is also a snap-shot of our ongoing efforts towards low-level translation and experimenting with unique features.
By default, PyPy is a Python version that works completely with new-style-classes semantics. However, support for old-style classes is still available. Implementations, mostly as user-level code, of their metaclass and instance object are included and can be re-made the default with the --oldstyle option.
In PyPy, bytecode interpretation and object manipulations are well separated between a bytecode interpreter and an object space which implements operations on objects. PyPy comes with experimental object spaces augmenting the standard one through delegation:
- an experimental object space that does extensive tracing of bytecode and object operations;
- the 'thunk' object space that implements lazy values and a 'become' operation that can exchange object identities.
These spaces already give a glimpse in the flexibility potential of PyPy. See demo/fibonacci.py and demo/sharedref.py for examples about the 'thunk' object space.
The 0.6 release also contains a snapshot of our translation-efforts to lower level languages. For that we have developed an annotator which is capable of inferring type information across our code base. The annotator right now is already capable of successfully type annotating basically all of PyPy code-base, and is included with 0.6.
From type annotated code, low-level code needs to be generated. Backends for various targets (C, LLVM,...) are included; they are all somehow incomplete and have been and are quite in flux. What is shipped with 0.6 is able to deal with more or less small/medium examples.
Ongoing work and near term goals
Generating low-level code is the main area we are hammering on in the next months; our plan is to produce a PyPy version in August/September that does not need to be interpreted by CPython anymore and will thus run considerably faster than the 0.6 preview release.
PyPy has been a community effort from the start and it would not have got that far without the coding and feedback support from numerous people. Please feel free to give feedback and raise questions.
contact points: http://pypy.org/contact.html
contributor list: contributor.html
Armin Rigo, Samuele Pedroni,
Holger Krekel, Christian Tismer,
Carl Friedrich Bolz
PyPy development and activities happen as an open source project and with the support of a consortium funded by a two year EU IST research grant. Here is a list of partners of the EU project:
Heinrich-Heine University (Germany), AB Strakt (Sweden)
merlinux GmbH (Germany), tismerysoft GmbH(Germany)
Logilab Paris (France), DFKI GmbH (Germany)