# Fast numerical computation of eigenvalues

I am trying to calculate the eigenvalues of a large (say, n=1000 to 10000) sparse Hermitian square matrix using SAGE

numpy.linalg.eigvalsh(MyMatrix)

takes very long. I noticed it utilizes only a single core of my CPU.

How would one go about speeding the calculation? Specifically, I'm looking for a solution using parallel computation, or maybe something which is "more compiled".

Thank you.

I'm having a similar issue with Sage notebooks on my AMD machine! Maybe OpenBLAS is being forced to run in single-threaded mode? I wish I knew how to change this...