==========================
Announcing Numexpr 2.2
==========================
Numexpr is a fast numerical expression evaluator for NumPy. With it,
expressions that operate on arrays (like "3*a+4*b") are accelerated
and use less memory than doing the same calculation in Python.
It wears multi-threaded capabilities, as well as support for Intel's
VML library (included in Intel MKL), which allows an extremely fast
evaluation of transcendental functions (sin, cos, tan, exp, log...)
while squeezing the last drop of performance out of your multi-core
processors.
Its only dependency is NumPy (MKL is optional), so it works well as an
easy-to-deploy, easy-to-use, computational kernel for projects that
don't want to adopt other solutions that require more heavy
dependencies.
What's new
==========
This release is mainly meant to fix a problem with the license the
numexpr/win32/pthread.{c,h} files emulating pthreads on Windows. After
persmission from the original authors is granted, these files adopt
the MIT license and can be redistributed without problems. See issue
#109 for details
(https://code.google.com/p/numexpr/issues/detail?id=110).
Another important improvement is the new algorithm to decide the initial
number of threads to be used. This was necessary because by default,
numexpr was using a number of threads equal to the detected number of
cores, and this can be just too much for moder systems where this
number can be too high (and counterporductive for performance in many
cases). Now, the 'NUMEXPR_NUM_THREADS' environment variable is
honored, and in case this is not present, a maximum number of *8*
threads are setup initially. The new algorithm is fully described in
the Users Guide, in the note of 'General routines' section:
https://code.google.com/p/numexpr/wiki/UsersGuide#General_routines.
Closes #110.
In case you want to know more in detail what has changed in this
version, see:
http://code.google.com/p/numexpr/wiki/ReleaseNotes
or have a look at RELEASE_NOTES.txt in the tarball.
Where I can find Numexpr?
=========================
The project is hosted at Google code in:
http://code.google.com/p/numexpr/
You can get the packages from PyPI as well:
http://pypi.python.org/pypi/numexpr
Share your experience
=====================
Let us know of any bugs, suggestions, gripes, kudos, etc. you may
have.
Enjoy data!
--
Francesc Alted
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