Francesc Alted
2017-01-29 13:07:48 UTC
=========================
Announcing Numexpr 2.6.2
=========================
What's new
==========
This is a maintenance release that fixes several issues, with special
emphasis in keeping compatibility with newer NumPy versions. Also,
initial support for POWER processors is here. Thanks to Oleksandr
Pavlyk, Alexander Shadchin, Breno Leitao, Fernando Seiti Furusato and
Antonio Valentino for their nice contributions.
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!
Announcing Numexpr 2.6.2
=========================
What's new
==========
This is a maintenance release that fixes several issues, with special
emphasis in keeping compatibility with newer NumPy versions. Also,
initial support for POWER processors is here. Thanks to Oleksandr
Pavlyk, Alexander Shadchin, Breno Leitao, Fernando Seiti Furusato and
Antonio Valentino for their nice contributions.
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
Francesc Alted