[pypy-commit] pypy default: work out the release announcement

fijal noreply at buildbot.pypy.org
Mon Nov 21 08:52:25 CET 2011

Author: Maciej Fijalkowski <fijall at gmail.com>
Changeset: r49595:f0938cd9a6f1
Date: 2011-11-21 09:51 +0200

Log:	work out the release announcement

diff --git a/pypy/doc/release-1.7.0.rst b/pypy/doc/release-1.7.0.rst
--- a/pypy/doc/release-1.7.0.rst
+++ b/pypy/doc/release-1.7.0.rst
@@ -1,47 +1,94 @@
-PyPy 1.7
+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
+speed up should be vastly improved with this release. You can download the 1.7
+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. 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 23/64 or
+Windows 32. Windows 64 work is ongoing, but not yet 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 **20x** faster on some benchmarks.
+.. _`pypy 1.7 and cpython 2.7.1`: http://speed.pypy.org
-* numerous performance improvements, PyPy 1.7 is xxx faster than 1.6
+* Numerous performance improvements. There are too many examples which python
+  constructs now should behave faster to list them.
-* numerous bugfixes, compatibility fixes
+* Bugfixes and compatibility fixes with CPython.
-* windows fixes
+* Windows fixes.
-* stackless and JIT integration
-  (stackless is now in the same executable, but any loop using
-  stackless features will interrupt the JIT for now, so no real
-  performance improvement for now)
+* 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 progress - dtypes, numpy -> numpypy renaming
+* NumPy effort in PyPy was renamed numpypy. In order to try using it, simply
+  write::
-* brand new, faster, JSON encoder
+    import numpypy as numpy
-* improved memory footprint on some RPython modules, such as hashlib
+  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.
-* cpyext progress
+* 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 **2x** in some cases. It's about **20x** 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
-* list strategies
+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:
-* multi-dimensional arrays for numpy
+* 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
-* ARM backend
+* NumPy effort is progressing forward, with multi-dimensional arrays coming
+  soon.
-* PPC backend
+* There are two brand new JIT assembler backends, notably for the PowerPC and
+  ARM processors.
-Things we're working on with unclear ETA
-* windows 64 (?)
+It's maybe worth mentioning that we're running fundraising campaigns for
+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`_.
-* Py3k
-* SSE for numpy
-* specialized objects
+.. _`numpy proposal`: http://pypy.org/numpydonate.html
+.. _`py3k proposal`: http://pypy.org/py3donate.html
+.. _`general pot`: http://pypy.org

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