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| author | LukenShiro <lukenshiro@ngi.it> | 2010-10-24 22:08:18 -0400 |
|---|---|---|
| committer | Erik Hanson <erik@slackbuilds.org> | 2010-10-25 07:55:10 -0500 |
| commit | b50ce4827f10b0d9a33760dbb1c38a13b02c4c29 (patch) | |
| tree | 90e07fe019f853b309c6d17c1f006afcf64a6ae8 /development/numexpr/README | |
| parent | afda04164adf91e4a5b29ca949a028455c40a75f (diff) | |
| download | slackbuilds-b50ce4827f10b0d9a33760dbb1c38a13b02c4c29.tar.gz | |
development/numexpr: Added (numerical array expression evaluator)
Signed-off-by: dsomero <xgizzmo@slackbuilds.org>
Diffstat (limited to 'development/numexpr/README')
| -rw-r--r-- | development/numexpr/README | 14 |
1 files changed, 14 insertions, 0 deletions
diff --git a/development/numexpr/README b/development/numexpr/README new file mode 100644 index 0000000000..7d34e96b2d --- /dev/null +++ b/development/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. |