How does NumPy handle internal performance scaling with multi-threaded BLAS?

NumPy delegates heavy linear algebra to BLAS libraries that use multi-threading for scalability.

Libraries like OpenBLAS and Intel MKL parallelize matrix operations across CPU cores. They split matrices into blocks and assign computations to multiple threads. NumPy itself is mostly single-threaded at the Python level, but performance scales through these backend libraries. Thread efficiency depends on matrix size and memory bandwidth limitations.