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| author |   LukenShiro | 2012-05-01 18:06:18 +0200 |
|---|---|---|
| committer |   Erik Hanson | 2012-05-07 19:18:06 +0200 |
| commit | d835fad7a703af3c8f8d65d519d30c9e179060d1 (patch) | |
| tree | 02e9fa21d02e4d1a5f46476117be8c1d51f7ae0b /python/numexpr/README | |
| parent | 0ecfcb7153a392bff1888dbecc7a30a6e51b520d (diff) | |
| download | slackbuilds-d835fad7a703af3c8f8d65d519d30c9e179060d1.tar.gz | |
python/numexpr: Updated for version 2.0.1 moved from development.
Signed-off-by: dsomero <xgizzmo@slackbuilds.org>
Diffstat (limited to 'python/numexpr/README')
| -rw-r--r-- | python/numexpr/README | 14 |
1 files changed, 14 insertions, 0 deletions
diff --git a/python/numexpr/README b/python/numexpr/README new file mode 100644 index 0000000000..7d34e96b2d --- /dev/null +++ b/python/numexpr/README @@ -0,0 +1,14 @@ +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. |