SciPy SVD vs. Numpy SVD

Question

Both SciPy and Numpy have built in functions for singular value decomposition (SVD). The commands are basically scipy.linalg.svd and numpy.linalg.svd.

Answer 1

From the FAQ page, it says scipy.linalg submodule provides a more complete wrapper for the Fortran LAPACK library whereas numpy.linalg tries to be able to build independent of LAPACK.

I did some benchmarks for the different implementation of the svd functions and found scipy.linalg.svd is faster than the numpy counterpart:

However, jax wrapped numpy, aka jax.numpy.linalg.svd is even faster:

Full notebook for the benchmarks are available here.

Answer 2

Apart from the error checking, the actual work seems to be done within lapack both with numpy and scipy.

Without having done any benchmarking, I guess the performance should be identical.