========================= 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