Improving performance in matrix operations

[Drimades]

I'm doing some tests with operations on numpy matrices in Python. As an example, it takes about 3000 seconds to compute eigenvalues and eigenvectors using scipy.linalg.eig(a) for a matrix 6000x6000. Is it an acceptable time? Any suggestions to improve? Does C++ perform better with matrices? Another thing to consider is that matrices I'm processing are heavily sparse.
Do they implement any parallelism? While my code is running, one of my cores is 100% busy, the other one 30% busy.