How does NumPy manage cache efficiency in large matrix operations?

NumPy improves cache efficiency by operating on contiguous memory blocks and optimizing loop order.

Matrix operations are designed to maximize spatial and temporal locality. When possible, NumPy accesses memory in row-major order (C-contiguous layout), ensuring sequential cache line usage. BLAS libraries further optimize this using block multiplication and cache tiling techniques.