How does NumPy handle internal slicing performance with large step sizes?

Slicing with steps modifies stride values, but large steps reduce cache efficiency.

When slicing with a step (e.g., arr[::k]), NumPy adjusts strides to skip memory locations. While this is still a view, large step sizes lead to poor spatial locality, increasing cache misses and reducing SIMD efficiency.