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python/numexpr: Wrap README at 72 columns.

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Signed-off-by: B. Watson <yalhcru@gmail.com>
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B. Watson 2022-03-14 11:05:27 -04:00
parent 7cc2f572fb
commit 1d54c7a39c
1 changed files with 12 additions and 11 deletions
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23
python/numexpr/README
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@ -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).