How does NumPy handle internal optimization of dot product operations?

Dot product operations are delegated to optimized BLAS libraries when possible.

NumPy uses highly optimized BLAS implementations (like OpenBLAS, MKL) for dot product and matrix multiplication. These libraries implement blocking, SIMD, and multi-threading. If BLAS is unavailable, NumPy falls back to internal C loops, which are slower. The operation is also sensitive to memory layout, preferring contiguous arrays for performance.