========================== 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 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 now 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