Python Numpy Data Types Performance

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Half precision arithmetic (float16) is something which must be “emulated” by numpy I guess, as there are no corresponding types in the underlying C language (and in the appropriate processor instructions) for it. On the other hand, single precision (float32) and double precision (float64) operations can be done very efficiently using native data types.

As of the good performance for single precision operations: Modern processors have efficient units for vectorized floating point arithmetics (e.g. AVX) as it is also needed for good multimedia performance.

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