numpy faster than numba and cython , how to improve numba code

Question

I have a simple example here to help me understand using numba and cython. I am `new to both numba and cython. I\'ve tried my best with to incorporate all the tricks to make

Answer 1

Add parallelization. In Numba that just involves making the outer loop prange and adding parallel=True to the jit options:

@numba.jit( nopython=True,parallel=True)    
def nb_expsum2(x):
    nx, ny = x.shape
    val = 0.0
    for ix in numba.prange(nx):
        for iy in range(ny):
            val += np.exp( x[ix, iy]   )
    return val

On my PC that gives a 3.2 times speedup over the non-parallel version. That said on my PC both Numba and Cython beat Numpy as written.

You can also do parallelization in Cython - I haven't tested it here but I'd expect to to be similar to Numba in performance. (Note also that for Cython you can get nx and ny from x.shape[0] and x.shape[1] so you don't have to turn off bounds-checking then rely entirely on user inputs to keep within the bounds).