======================== Announcing Numexpr 1.2 ======================== 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.
The main feature added in this version is the support of the Intel VML library (many thanks to Gregor Thalhammer for his nice work on this!). In addition, when the VML support is on, several processors can be used in parallel (see the new `set_vml_num_threads()` function). When the VML support is on, the computation of transcendental functions (like trigonometrical, exponential, logarithmic, hyperbolic, power...) can be accelerated quite a few. Typical speed-ups when using one single core for contiguous arrays are around 3x, with peaks of 7.5x (for the pow() function). When using 2 cores the speed-ups are around 4x and 14x respectively. In case you want to know more in detail what has changed in this version, have a look at the release notes: http://code.google.com/p/numexpr/wiki/ReleaseNotes Where I can find Numexpr? ========================= The project is hosted at Google code in: http://code.google.com/p/numexpr/ And you can get the packages from PyPI as well: http://pypi.python.org/pypi How it works? ============= See: http://code.google.com/p/numexpr/wiki/Overview for a detailed description of the package. Share your experience ===================== Let us know of any bugs, suggestions, gripes, kudos, etc. you may have. Enjoy! -- Francesc Alted -- http://mail.python.org/mailman/listinfo/python-announce-list Support the Python Software Foundation: http://www.python.org/psf/donations.html
