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authorB. Watson <yalhcru@gmail.com>2022-03-14 11:05:27 -0400
committerB. Watson <yalhcru@gmail.com>2022-03-17 12:37:55 -0400
commit1d54c7a39c072c8cd15e15108228dc704a3e105b (patch)
treefe208711f27b4c6008e0bcc9ddae699b85bdb329 /python/numexpr
parent7cc2f572fba5a2cd5123053b7cba6b573b6989f8 (diff)
python/numexpr: Wrap README at 72 columns.
Signed-off-by: B. Watson <yalhcru@gmail.com>
Diffstat (limited to 'python/numexpr')
-rw-r--r--python/numexpr/README23
1 files changed, 12 insertions, 11 deletions
diff --git a/python/numexpr/README b/python/numexpr/README
index 30cffee3aa1fe..5ef2b91721ca6 100644
--- a/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).