mirror of
https://git.FreeBSD.org/ports.git
synced 2024-12-05 01:55:52 +00:00
714db4465a
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, numexpr has support for the Intel VML (Vector Math Library) -- integrated in Intel MKL (Math Kernel Library) --, allowing nice speed-ups when computing transcendental functions (like trigonometrical, exponentials...) on top of Intel-compatible platforms. This support also allows to use multiple cores in your computations. WWW: http://code.google.com/p/numexpr/ PR: ports/148372 Submitted by: Ju Pengfei <jupengfei@gmail.com> Feature safe: yes
17 lines
851 B
Plaintext
17 lines
851 B
Plaintext
numexpr - Fast numerical array expression evaluator for Python and NumPy.
|
|
|
|
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, numexpr has support for the Intel VML (Vector Math Library) -- integrated
|
|
in Intel MKL (Math Kernel Library) --, allowing nice speed-ups when computing
|
|
transcendental functions (like trigonometrical, exponentials...) on top of
|
|
Intel-compatible platforms. This support also allows to use multiple cores in
|
|
your computations.
|
|
|
|
WWW: http://code.google.com/p/numexpr/
|