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authorLukenShiro <lukenshiro@ngi.it>2012-05-01 12:06:18 -0400
committerErik Hanson <erik@slackbuilds.org>2012-05-07 12:18:06 -0500
commitd835fad7a703af3c8f8d65d519d30c9e179060d1 (patch)
tree02e9fa21d02e4d1a5f46476117be8c1d51f7ae0b /python/numexpr/README
parent0ecfcb7153a392bff1888dbecc7a30a6e51b520d (diff)
downloadslackbuilds-d835fad7a703af3c8f8d65d519d30c9e179060d1.tar.gz
python/numexpr: Updated for version 2.0.1 moved from development.
Signed-off-by: dsomero <xgizzmo@slackbuilds.org>
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+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.