PyPy 2.0 beta 1 released
We're pleased to announce the 2.0 beta 1 release of PyPy. This release is not a typical beta, in a sense the stability is the same or better than 1.9 and can be used in production. It does however include a few performance regressions documented below that don't allow us to label is as 2.0 final. (It also contains many performance improvements.) The main features of this release are support for ARM processor and compatibility with CFFI. It also includes numerous improvements to the numpy in pypy effort, cpyext and performance. You can download the PyPy 2.0 beta 1 release here: http://pypy.org/download.html What is PyPy? ============= PyPy is a very compliant Python interpreter, almost a drop-in replacement for CPython 2.7.3. It's fast (`pypy 2.0 beta 1 and cpython 2.7.3`_ performance comparison) due to its integrated tracing JIT compiler. This release supports x86 machines running Linux 32/64, Mac OS X 64 or Windows 32. It also supports ARM machines running Linux. Windows 64 work is still stalling, we would welcome a volunteer to handle that. .. _`pypy 2.0 beta 1 and cpython 2.7.3`: http://bit.ly/USXqpP How to use PyPy? ================ We suggest using PyPy from a `virtualenv`_. Once you have a virtualenv installed, you can follow instructions from `pypy documentation`_ on how to proceed. This document also covers other `installation schemes`_. .. _`pypy documentation`: http://doc.pypy.org/en/latest/getting-started.html#installing-using-virtuale... .. _`virtualenv`: http://www.virtualenv.org/en/latest/ .. _`installation schemes`: http://doc.pypy.org/en/latest/getting-started.html#installing-pypy .. _`PyPy and pip`: http://doc.pypy.org/en/latest/getting-started.html#installing-pypy Regressions =========== Reasons why this is not PyPy 2.0: * the ``ctypes`` fast path is now slower than it used to be. In PyPy 1.9 ``ctypes`` was either incredibly faster or slower than CPython depending whether you hit the fast path or not. Right now it's usually simply slower. We're probably going to rewrite ``ctypes`` using ``cffi``, which will make it universally faster. * ``cffi`` (an alternative to interfacing with C code) is very fast, but it is missing one optimization that will make it as fast as a native call from C. * ``numpypy`` lazy computation was disabled for the sake of simplicity. We should reenable this for the final 2.0 release. Highlights ========== * ``cffi`` is officially supported by PyPy. You can install it normally by using ``pip install cffi`` once you have installed `PyPy and pip`_. The corresponding ``0.4`` version of ``cffi`` has been released. * ARM is now an officially supported processor architecture. PyPy now work on soft-float ARM/Linux builds. Currently ARM processors supporting the ARMv7 and later ISA that include a floating-point unit are supported. * This release contains the latest Python standard library 2.7.3 and is fully compatible with Python 2.7.3. * It does not however contain hash randomization, since the solution present in CPython is not solving the problem anyway. The reason can be found on the `CPython issue tracker`_. * ``gc.get_referrers()`` is now faster. * Various numpy improvements. The list includes: * axis argument support in many places * full support for fancy indexing * ``complex128`` and ``complex64`` dtypes * `JIT hooks`_ are now a powerful tool to introspect the JITting process that PyPy performs. * ``**kwds`` usage is much faster in the typical scenario * operations on ``long`` objects are now as fast as in CPython (from roughly 2x slower) * We now have special strategies for ``dict``/``set``/``list`` which contain unicode strings, which means that now such collections will be both faster and more compact. .. _`cpython issue tracker`: http://bugs.python.org/issue14621 .. _`jit hooks`: http://doc.pypy.org/en/latest/jit-hooks.html Things we're working on ======================= There are a few things that did not make it to the 2.0 beta 1, which are being actively worked on. Greenlets support in the JIT is one that we would like to have before 2.0 final. Two important items that will not make it to 2.0, but are being actively worked on, are: * Faster JIT warmup time. * Software Transactional Memory. Cheers, Maciej Fijalkowski, Armin Rigo and the PyPy team
Hey , ARM Build! thats mean we can run Pypy in android now right? I am building my own Python for Android with required dependencies , i am gonna test pypy on android too! Thanks , good job pypy team! When do you think numpypy and pypy c extension is complete enought to support matplotlib/scipy/pylab kit (they have a lot of other C Dependencies ..) Thanks! On Thu, Nov 22, 2012 at 6:24 PM, Maciej Fijalkowski <fijall@gmail.com>wrote:
Hey , ARM Build! thats mean we can run Pypy in android now right? I am building my own Python for Android with required dependencies , i am gonna test pypy on android too! Thanks , good job pypy team! When do you think numpypy and pypy c extension is complete enought to support matplotlib/scipy/pylab kit (they have a lot of other C Dependencies ..) Thanks! On Thu, Nov 22, 2012 at 6:24 PM, Maciej Fijalkowski <fijall@gmail.com>wrote:
participants (3)
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Maciej Fijalkowski -
Phyo Arkar -
Stefan Behnel