Hi everyone,
NumExpr 2.10.2 provides wheels for Python 2.13 for first time. Also, there is better support for CPUs that do not have a power of 2 number of cores. Finally, numexpr is allowed to run with the multithreading package in Python.
Project documentation is available at:
http://numexpr.readthedocs.io/
- Better support for CPUs that do not have a power of 2 number of cores. See #479 and #490. Thanks to @avalentino.
- Allow numexpr to run with the multithreading package in Python. See PR #496. Thanks to @emmaai
- Wheels for Python 3.13 are now provided.
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 has 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.
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
Documentation is hosted at:
http://numexpr.readthedocs.io/en/latest/
Let us know of any bugs, suggestions, gripes, kudos, etc. you may have.
Enjoy data!