**Update 2016-01-16: Numba 0.23 released and tested – results added at the end of this post**

A while back I was using Numba to accelerate some image processing I was doing and noticed that there was a difference in speed whether I used functions from NumPy or their equivalent from the standard Python math package within the function I was accelerating using Numba. If memory serves, I was using the exp function for something and noticed that replacing numpy.exp with math.exp in the function I had decorated with @jit made a noticeable difference in running time. I didn’t investigate this any further at the time, but now, several versions of Numba and NumPy later, I wanted to find out what was causing this difference and what the current status was in terms of which is faster to use. Continue reading →

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