PyPy 1.9 - Yard Wolf
We're pleased to announce the 1.9 release of PyPy. This release brings mostly bugfixes, performance improvements, other small improvements and overall progress on the numpypy effort. It also brings an improved situation on Windows and OS X.
You can download the PyPy 1.9 release here:
What is PyPy?
PyPy is a very compliant Python interpreter, almost a drop-in replacement for CPython 2.7. It's fast (pypy 1.9 and cpython 2.7.2 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. Windows 64 work is still stalling, we would welcome a volunteer to handle that.
Thanks to our donors
But first of all, we would like to say thank you to all people who donated some money to one of our four calls:
Thank you all for proving that it is indeed possible for a small team of programmers to get funded like that, at least for some time. We want to include this thank you in the present release announcement even though most of the work is not finished yet. More precisely, neither Py3k nor STM are ready to make it in an official release yet: people interested in them need to grab and (attempt to) translate PyPy from the corresponding branches (respectively py3k and stm-thread).
- This release still implements Python 2.7.2.
- Many bugs were corrected for Windows 32 bit. This includes new functionality to test the validity of file descriptors; and correct handling of the calling convensions for ctypes. (Still not much progress on Win64.) A lot of work on this has been done by Matti Picus and Amaury Forgeot d'Arc.
- Improvements in cpyext, our emulator for CPython C extension modules. For example PyOpenSSL should now work. We thank various people for help.
- Sets now have strategies just like dictionaries. This means for example that a set containing only ints will be more compact (and faster).
- A lot of progress on various aspects of numpypy. See the numpy-status page for the automatic report.
- It is now possible to create and manipulate C-like structures using the PyPy-only _ffi module. The advantage over using e.g. ctypes is that _ffi is very JIT-friendly, and getting/setting of fields is translated to few assembler instructions by the JIT. However, this is mostly intended as a low-level backend to be used by more user-friendly FFI packages, and the API might change in the future. Use it at your own risk.
- The non-x86 backends for the JIT are progressing but are still not merged (ARMv7 and PPC64).
- JIT hooks for inspecting the created assembler code have been improved. See JIT hooks documentation for details.
- select.kqueue has been added (BSD).
- Handling of keyword arguments has been drastically improved in the best-case scenario: proxy functions which simply forwards *args and **kwargs to another function now performs much better with the JIT.
- List comprehension has been improved.
There will be a corresponding 1.9 release of JitViewer which is guaranteed to work with PyPy 1.9. See the JitViewer docs for details.
Cheers, The PyPy Team