How does NumPy manage stride-based broadcasting without memory allocation?

Broadcasting is implemented using stride manipulation without duplicating memory.

When broadcasting arrays of different shapes, NumPy expands dimensions of size 1 by setting their stride to zero. This means the same memory location is reused across expanded dimensions. This zero-copy mechanism allows efficient vectorized computation without allocating additional buffers.