======================== PyPy 1.6 - kickass panda ======================== We're pleased to announce the 1.6 release of PyPy. This release brings a lot of bugfixes and performance improvements over 1.5, and improves support for Windows 32bit and OS X 64bit. This version fully implements Python 2.7.1 and has beta level support for loading CPython C extensions. You can download it here: http://pypy.org/download.html What is PyPy? ============= PyPy is a very compliant Python interpreter, almost a drop-in replacement for CPython 2.7.1. It's fast (`pypy 1.5 and cpython 2.6.2`_ performance comparison) due to its integrated tracing JIT compiler. This release supports x86 machines running Linux 32/64 or Mac OS X. Windows 32 is beta (it roughly works but a lot of small issues have not been fixed so far). Windows 64 is not yet supported. The main topics of this release are speed and stability: on average on our benchmark suite, PyPy 1.6 is between **20% and 30%** faster than PyPy 1.5, which was already much faster than CPython on our set of benchmarks. The speed improvements have been made possible by optimizing many of the layers which compose PyPy. In particular, we improved: the Garbage Collector, the JIT warmup time, the optimizations performed by the JIT, the quality of the generated machine code and the implementation of our Python interpreter. .. _`pypy 1.5 and cpython 2.6.2`: http://speed.pypy.org Highlights ========== * Numerous performance improvements, overall giving considerable speedups: - better GC behavior when dealing with very large objects and arrays - **fast ctypes:** now calls to ctypes functions are seen and optimized by the JIT, and they are up to 60 times faster than PyPy 1.5 and 10 times faster than CPython - improved generators(1): simple generators now are inlined into the caller loop, making performance up to 3.5 times faster than PyPy 1.5. - improved generators(2): thanks to other optimizations, even generators that are not inlined are between 10% and 20% faster than PyPy 1.5. - faster warmup time for the JIT - JIT support for single floats (e.g., for ``array('f')``) - optimized dictionaries: the internal representation of dictionaries is now dynamically selected depending on the type of stored objects, resulting in faster code and smaller memory footprint. For example, dictionaries whose keys are all strings, or all integers. Other dictionaries are also smaller due to bugfixes. * JitViewer: this is the first official release which includes the JitViewer, a web-based tool which helps you to see which parts of your Python code have been compiled by the JIT, down until the assembler. The `jitviewer`_ 0.1 has already been release and works well with PyPy 1.6. * The CPython extension module API has been improved and now supports many more extensions. For information on which one are supported, please refer to our `compatibility wiki`_. * Multibyte encoding support: this was of of the last areas in which we were still behind CPython, but now we fully support them. * Preliminary support for NumPy: this release includes a preview of a very fast NumPy module integrated with the PyPy JIT. Unfortunately, this does not mean that you can expect to take an existing NumPy program and run it on PyPy, because the module is still unfinished and supports only some of the numpy API. However, barring some details, what works should be blazingly fast :-) * Bugfixes: since the 1.5 release we fixed 53 bugs in our `bug tracker`_, not counting the numerous bugs that were found and reported through other channels than the bug tracker. Cheers, Hakan Ardo, Carl Friedrich Bolz, Laura Creighton, Antonio Cuni, Maciej Fijalkowski, Amaury Forgeot d'Arc, Alex Gaynor, Armin Rigo and the PyPy team .. _`jitviewer`: http://morepypy.blogspot.com/2011/08/visualization-of-jitted-code.html .. _`bug tracker`: https://bugs.pypy.org .. _`compatibility wiki`: https://bitbucket.org/pypy/compatibility/wiki/Home