How does NumPy internally handle high-performance reductions with multi-axis operations?

NumPy chooses optimal traversal order based on stride layout for multi-axis reductions.

For operations like sum over multiple axes, NumPy computes the most efficient iteration order by analyzing memory strides. It collapses axes where possible and processes contiguous chunks to maximize cache performance. This reduces cache misses and improves throughput.