Parallel loops with Numba -- no parallelization with prange

Question

I am implementing a sequential algorithm (Kalman Filter) with a particular structure where a lot of inner looping can be done in parallel. I need to get as much performance out of this function as possible. Currently, it runs in about 600ms on my machine with representative data inputs (n, p = 12, d = 3, T = 3000)

I have used @numba.jit with nopython=True, parallel=True and annotated my ranges with numba.prange . However, even with very large data inputs (n > 5000) there is clearly no parallelism occurring (based on just looking at cores with top ).

There is quite a bit of code here, I'm showing only the main chunk. Is there a reason Numba wouldn't be able to parallelize the array operations under the prange ? I have also checked numba.config.NUMBA_NUM_THREADS (it is 8) and played with different numba.config.THREADING_LAYER (it is currently 'tbb' ). I have also tried with both the openblas and the MKL versions of numpy+scipy, the MKL version appears to be slightly slower, and still no parallelization.

The annotation is:

@numba.jit(nopython=True, cache=False, parallel=True,
           fastmath=True, nogil=True)

And the main part of the function:

P = np.empty((T + 1, n, p, d, d))
m = np.empty((T + 1, n, p, d))

P[0] = P0
m[0] = m0

phi = 0.0
Xt = np.empty((n, p)

for t in range(1, T + 1):
    sum_P00 = 0.0
    v = y[t - 1]

    # Purely for convenience, little performance impact
    for tau in range(1, p + 1):
        Xt[:, tau - 1] = X[p + t - 1 - tau]

    # Predict
    for i in numba.prange(n):
        for tau in range(p):
            # Prediction step
            m[t, i, tau] = Phi[i, tau] @ m[t - 1, i, tau]
            P[t, i, tau] = Phi[i, tau] @ P[t - 1, i, tau] @ Phi[i, tau].T

    # Auxiliary gain variables
    for i in numba.prange(n):
        for tau in range(p):
            v = v - Xt[i, tau] * m[t, i, tau, 0]
            sum_P00 = sum_P00 + P[t, i, tau, 0, 0]

    # Energy function update
    s = np.linalg.norm(Xt)**2 * sum_P00 + sv2
    phi += np.pi * s + 0.5 * v**2 / s

    # Update
    for i in numba.prange(n):
        for tau in range(p):
            k = Xt[i, tau] * P[t, i, tau, :, 0]  # Gain
            m[t, i, tau] = m[t, i, tau] + (v / s) * k
            P[t, i, tau] = P[t, i, tau] + (k / s) @ k.T

Answer 1

It appears to simply have been a problem with running interactively in Ipython. Running a test script from the console leads to parallel execution, as expected.