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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.
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The numexpr package evaluates multiple-operator array expressions many times
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faster than NumPy can. It accepts the expression as a string, analyzes it,
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rewrites it more efficiently, and compiles it to faster Python code on the fly.
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It's the next best thing to writing the expression in C and compiling it with
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a specialized just-in-time (JIT) compiler, i.e. it does not require a compiler
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at runtime.
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Also, numexpr has support for the Intel VML (Vector Math Library) -- integrated
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in Intel MKL (Math Kernel Library) --, allowing nice speed-ups when computing
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transcendental functions (like trigonometrical, exponentials...) on top of
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Intel-compatible platforms. This support also allows to use multiple cores in
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your computations.
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WWW: http://code.google.com/p/numexpr/
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