How does NumPy handle internal loop blocking for large matrix operations?

NumPy and BLAS libraries use loop blocking (tiling) to improve cache reuse in large matrix computations.

Loop blocking divides large matrices into smaller sub-blocks that fit into CPU cache (L1/L2/L3). Instead of iterating row-by-row over huge matrices, computations are performed on these blocks to reduce cache misses. NumPy itself delegates matrix multiplication to BLAS implementations like OpenBLAS or MKL, which implement sophisticated blocking strategies and SIMD acceleration.