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RPM: python3-module-numexpr
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Current version: 2.8.3-alt1
Build date: 17 july 2022, 23:12 ( 92.9 weeks ago )
Size: 100.61 Kb
Home page: https://github.com/pydata/numexpr
License: MIT
Summary: Fast numerical array expression evaluator for Python and NumPy
Description:
List of contributors
List of rpms provided by this srpm:
ACL:
Build date: 17 july 2022, 23:12 ( 92.9 weeks ago )
Size: 100.61 Kb
Home page: https://github.com/pydata/numexpr
License: MIT
Summary: Fast numerical array expression evaluator for Python and NumPy
Description:
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.
Current maintainer: Vitaly Lipatov 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.
List of contributors
- python3-module-numexpr
- python3-module-numexpr-debuginfo