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Contents of README: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. 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). This requires numpy. |
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Parent Directory - README 07-May-2012 17:26 785 numexpr.SlackBuild 07-May-2012 17:26 2.7K numexpr.info 07-May-2012 17:26 293 slack-desc 07-May-2012 17:26 868
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