ANN: numexpr 2.6.1 released

[Francesc Alted]

=========================
 Announcing Numexpr 2.6.1
=========================

What's new
==========

This is a maintenance release that fixes a performance regression in
some situations. More specifically, the BLOCK_SIZE1 constant has been
set to 1024 (down from 8192). This allows for better cache utilization
when there are many operands and with VML.  Fixes #221.

Also, support for NetBSD has been added.  Thanks to Thomas Klausner.

In case you want to know more in detail what has changed in this
version, see:

https://github.com/pydata/numexpr/blob/master/RELEASE_NOTES.rst


What's Numexpr
==============

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
MKL (Math Kernel Library), 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.  Look here for a some benchmarks of numexpr using MKL:

https://github.com/pydata/numexpr/wiki/NumexprMKL

Its only dependency is NumPy (MKL is optional), so it works well as an
easy-to-deploy, easy-to-use, computational engine for projects that
don't want to adopt other solutions requiring more heavy dependencies.

Where I can find Numexpr?
=========================

The project is hosted at GitHub in:

https://github.com/pydata/numexpr

You can get the packages from PyPI as well (but not for RC releases):

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