I have noticed that there is a significant performance difference when indexing a numpy array with np.uint64 vs npint64. Take the following simple contrived example: import time import numpy as np from numba import njit @njit def some_func(a, idx): for _ in range(100_000_000_000): …

This is known and expected behavior. Think about it, if an index could be negative you have to check if it was negative and if so do the index wraparound calculation. This adds time but even if all the indices are positive then the presence of the checking code also prohibits vectorization. This …

@DrTodd13 Thank you so much for enlightening me! I learned something new. Would you happen to know what the type would be in for i in range(10)? Is i a uint64 or an int64? I plan to add i to another numpy array of dtype=np.uint64 and then use the resulting sum to index into another numpy array: idx…

thanks @seanlaw for bringing it up and @DrTodd13 for the explanation! I have a lot of code that uses indexing, I now wonder if I can squeeze some more performance using numba.uint64(i) I am also wondering whether the type of range with 1 argument shouldn’t be an unint64, since it will always be pos…

Related (if tangentially) issue here

Thank you @DrTodd13 . If I have many integers, I wonder if casting with numba.uint64(i) would create the same/similar cost as needing to check for negative integers before using the value as an array index?

Casting has almost no overhead.

Uint64 vs int64 indexing performance difference

Numba Support: How do I do ...?

DrTodd13 August 15, 2022, 12:53am 4

“i” will be typed as int64. However, you can use numba.uint64(i) to create a new variable from it that is unsigned.

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Uint64 vs int64 indexing performance difference

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