How to implement parallel, delayed in such a way that the parallelized for loop stops when output goes below a threshold?

Suppose I have the following code:

from scipy import *
import multiprocessing as mp
num_cores = mp.cpu_count()
from joblib import Parallel, delayed
import matplotlib.pyplot as plt

def func(x,y):
    return y/x
def main(y, xmin,xmax, dx):
    x = arange(xmin,xmax,dx)
    output = Parallel(n_jobs=num_cores)(delayed(func)(i, y) for i in x)
    return x, asarray(output)
def demo():
    x,z = main(2.,1.,30.,.1)
    plt.plot(x,z, label='All values')
    plt.plot(x[z>.1],z[z>.1], label='desired range') ## This is better to do in main()
    plt.show()

demo()

I want to calculate output only until output > a given number (it can be assumed that elements of output decreases monotonically with increase of x) and then stop (NOT calculating for all values of x and then sorting, that's inefficient for my purpose). Is there any way to do that using Parallel, delayed or any other multiprocessing?

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