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14 lines
785 B
Text
14 lines
785 B
Text
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
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fly. It's the next best thing to writing the expression in C and compiling
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it with a specialized just-in-time (JIT) compiler, i.e. it does not require
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a compiler at runtime.
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Also, and since version 1.4, numexpr implements support for multi-threading
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computations straight into its internal virtual machine, written in C. This
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allows to bypass the GIL in Python, and allows near-optimal parallel
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performance in your vector expressions, most specially on CPU-bounded
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operations (memory-bounded were already the strong point of Numexpr).
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This requires numpy.
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