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| author | B. Watson <yalhcru@gmail.com> | 2022-03-14 11:05:27 -0400 |
|---|---|---|
| committer | B. Watson <yalhcru@gmail.com> | 2022-03-17 12:37:55 -0400 |
| commit | 1d54c7a39c072c8cd15e15108228dc704a3e105b (patch) | |
| tree | fe208711f27b4c6008e0bcc9ddae699b85bdb329 /python/numexpr/README | |
| parent | 7cc2f572fba5a2cd5123053b7cba6b573b6989f8 (diff) | |
| download | slackbuilds-1d54c7a39c072c8cd15e15108228dc704a3e105b.tar.gz | |
python/numexpr: Wrap README at 72 columns.
Signed-off-by: B. Watson <yalhcru@gmail.com>
Diffstat (limited to 'python/numexpr/README')
| -rw-r--r-- | python/numexpr/README | 23 |
1 files changed, 12 insertions, 11 deletions
diff --git a/python/numexpr/README b/python/numexpr/README index 30cffee3aa..5ef2b91721 100644 --- a/python/numexpr/README +++ b/python/numexpr/README @@ -1,12 +1,13 @@ -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. +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). +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). |