# PyPy 2.4 - Snow White

We’re pleased to announce PyPy 2.4, which contains significant performance enhancements and bug fixes.

You can download the PyPy 2.4.0 release here:

> http://pypy.org/download.html

We would like to thank our donors for the continued support of the PyPy project, and for those who donate to our three sub-projects. We’ve shown quite a bit of progress, but we’re slowly running out of funds. Please consider donating more, or even better convince your employer to donate, so we can finish those projects! We would like to also point out that in September, the Python Software Foundation will match funds for any donations up to $10k! The three sub-projects are:

- Py3k (supporting Python 3.x): We have released a Python 3.2.5 compatible version

    we call PyPy3 2.3.1, and are working toward a Python 3.3 compatible version

- STM (software transactional memory): We have released a first working version, and continue to try out new promising paths of achieving a fast multithreaded Python

- NumPy which requires installation of our fork of upstream numpy, available on bitbucket

## What is PyPy?

PyPy is a very compliant Python interpreter, almost a drop-in replacement for CPython 2.7. It’s fast (pypy 2.4 and cpython 2.7.x performance comparison) due to its integrated tracing JIT compiler.

This release supports x86 machines on most common operating systems (Linux 32/64, Mac OS X 64, Windows, and OpenBSD), as well as newer ARM hardware (ARMv6 or ARMv7, with VFPv3) running Linux.

While we support 32 bit python on Windows, work on the native Windows 64 bit python is still stalling, we would welcome a volunteer to handle that.

## Highlights

Benchmarks improved after internal enhancements in string and bytearray handling, and a major rewrite of the GIL handling. This means that external calls are now a lot faster, especially the CFFI ones. It also means better performance in a lot of corner cases with handling strings or bytearrays. The main bugfix is handling of many socket objects in your program which in the long run used to “leak” memory.

PyPy now uses Python 2.7.8 standard library.

We fixed a memory leak in IO in the sandbox code

We welcomed more than 12 new contributors, and conducted two Google Summer of Code projects, as well as other student projects not directly related to Summer of Code.

Issues reported with our previous release were fixed after reports from users on our new issue tracker at https://bitbucket.org/pypy/pypy/issues or on IRC at #pypy. Here is a summary of the user-facing changes; for more information see whats-new:

- Reduced internal copying of bytearray operations
- Tweak the internal structure of StringBuilder to speed up large string handling, which becomes advantageous on large programs at the cost of slightly slower small benchmark type programs.
- Boost performance of thread-local variables in both unjitted and jitted code, this mostly affects errno handling on linux, which makes external calls faster.
- Move to a mixed polling and mutex GIL model that make mutlithreaded jitted code run much faster
- Optimize errno handling in linux (x86 and x86-64 only)
- Remove ctypes pythonapi and ctypes.PyDLL, which never worked on PyPy
- Fix performance regression on ufunc(<scalar>, <scalar>) in numpy
- Classes in the ast module are now distinct from structures used by the compiler, which simplifies and speeds up translation of our source code to the PyPy binary interpreter
- Upgrade stdlib from 2.7.5 to 2.7.8
- Win32 now links statically to zlib, expat, bzip, and openssl-1.0.1i. No more missing DLLs
- Many issues were resolved since the 2.3.1 release on June 8

We have further improvements on the way: rpython file handling, numpy linalg compatibility, as well as improved GC and many smaller improvements.

Please try it out and let us know what you think. We especially welcome success stories, we know you are using PyPy, please tell us about it!

Cheers

The PyPy Team