PyPy 1.7 - widening the sweet spot¶
We’re pleased to announce the 1.7 release of PyPy. As became a habit, this release brings a lot of bugfixes and performance improvements over the 1.6 release. However, unlike the previous releases, the focus has been on widening the “sweet spot” of PyPy. That is, classes of Python code that PyPy can greatly speed up should be vastly improved with this release. You can download the 1.7 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 1.7 and cpython 2.7.1 performance comparison) due to its integrated tracing JIT compiler.
This release supports x86 machines running Linux 32/64, Mac OS X 32/64 or Windows 32. Windows 64 work is ongoing, but not yet natively supported.
The main topic of this release is widening the range of code which PyPy can greatly speed up. On average on our benchmark suite, PyPy 1.7 is around 30% faster than PyPy 1.6 and up to 20 times faster on some benchmarks.
Numerous performance improvements. There are too many examples which python constructs now should behave faster to list them.
Bugfixes and compatibility fixes with CPython.
PyPy now comes with stackless features enabled by default. However, any loop using stackless features will interrupt the JIT for now, so no real performance improvement for stackless-based programs. Contact pypy-dev for info how to help on removing this restriction.
NumPy effort in PyPy was renamed numpypy. In order to try using it, simply write:
import numpypy as numpy
at the beginning of your program. There is a huge progress on numpy in PyPy since 1.6, the main feature being implementation of dtypes.
JSON encoder (but not decoder) has been replaced with a new one. This one is written in pure Python, but is known to outperform CPython’s C extension up to 2 times in some cases. It’s about 20 times faster than the one that we had in 1.6.
The memory footprint of some of our RPython modules has been drastically improved. This should impact any applications using for example cryptography, like tornado.
There was some progress in exposing even more CPython C API via cpyext.
Things that didn’t make it, expect in 1.8 soon¶
There is an ongoing work, which while didn’t make it to the release, is probably worth mentioning here. This is what you should probably expect in 1.8 some time soon:
Specialized list implementation. There is a branch that implements lists of integers/floats/strings as compactly as array.array. This should drastically improve performance/memory impact of some applications
NumPy effort is progressing forward, with multi-dimensional arrays coming soon.
There are two brand new JIT assembler backends, notably for the PowerPC and ARM processors.
It’s maybe worth mentioning that we’re running fundraising campaigns for NumPy effort in PyPy and for Python 3 in PyPy. In case you want to see any of those happen faster, we urge you to donate to numpy proposal or py3k proposal. In case you want PyPy to progress, but you trust us with the general direction, you can always donate to the general pot.