numexpr 2.0.1

The numexpr package evaluates multiple-operator array expressions many times faster than NumPy can. It accepts the expression as a string, analyzes it, rewrites it more efficiently, and compiles it to faster Python code on the fly. It's the next best thing to writing the expression in C and compiling it with a specialized just-in-time (JIT) compiler, i.e. it does not require a compiler at runtime.<br /><br />Also, and since version 1.4, numexpr implements support for multi-threading computations straight into its internal virtual machine, written in C. This allows to bypass the GIL in Python, and allows near-optimal parallel performance in your vector expressions, most specially on CPU-bounded operations (memory-bounded were already the strong point of Numexpr). See MultiThreadVM for more info on this.<br /><br />Finally, numexpr has support for the Intel VML (Vector Math Library) -- integrated in Intel MKL (Math Kernel Library) --, allowing nice speed-ups when computing transcendental functions (like trigonometrical, exponentials...) on top of Intel-compatible platforms. This support also allows to use multiple cores in your computations.

Requirements:	No special requirements
Platforms:	*nix, Linux
Keyword:	Compiler,  Computations,  Expression,  Expressions,  Faster,  Intel,  Library,  Memorybounded,  Numexpr,  Operations,  Parallel,  Performance,  Point,  Python,  Specially,  Strong,  Support
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