How does NumPy handle internal memory alignment for SIMD optimization?

NumPy aligns memory to improve SIMD vectorization efficiency in low-level loops.

SIMD (Single Instruction Multiple Data) operations require properly aligned memory for maximum efficiency. NumPy ensures alignment during allocation of arrays so that C-level loops can process multiple elements per CPU instruction. Misaligned data can reduce vectorization performance or trigger fallback paths.