As Stefan wrote, all you are really doing with larger matrix tests is testing the speed of the different BLAS implementations being used by your distributions of Julia and NumPy.
As I wrote in the other thread https://groups.google.com/d/msg/julia-users/Q96aPufg4S8/IBU9hW0xvWYJ the Vandermonde matrix generation is significantly faster for me in Julia than in Python (numpy using reference BLAS). Furthermore Vandermonde is not a good test with larger matrix sizes since you are basically testing the speed of multiplying things by infinity, which may not be representative of typical computations as it may incur overhead from handling overflows. For n=10000 I get the following results: Macbook Julia (Core(TM) i5-4258U) +/-1 matrix: 1.22s [1:n] matrix: 1.21s #mostly overflow rand matrix: 2.95s #mostly underflow Macbook NumPy +/-1 matrix: 3.96s [1:n] matrix: 5.04s #mostly overflow rand matrix: 5.46s #mostly underflow Linux Julia (Xeon(R) E7- 8850) +/-1 matrix: 2.18s [1:n] matrix: 1.89s #mostly overflow rand matrix: 4.36s #mostly underflow Linux NumPy +/-1 matrix: 9.38s [1:n] matrix: 10.64s #mostly overflow * rand matrix: 32.30s #mostly underflow * emits warnings: /usr/lib/python2.7/dist-packages/numpy/lib/twodim_base.py:515: RuntimeWarning: overflow encountered in power X[:, i] = x**(N - i - 1) /usr/lib/python2.7/dist-packages/numpy/lib/twodim_base.py:515: RuntimeWarning: invalid value encountered in power X[:, i] = x**(N - i - 1)
