In the past I have not needed to use parallel computing, because my python scripts aren't usually computationally intensive. Recently, I wrote the following for-loop and found that it takes on the order of 10 - 15 minutes to run:

```
cpx_var = np.linspace(0.20,0.80,301)
horn_var = np.linspace(0.20,0.80,301)
plag_var = np.linspace(0.05,0.1,26)
mag_var = np.linspace(0.02,0.06,21)
ap_var = np.linspace(0.002,0.006,3)

poss_comb = []
count=0

for i in cpx_var:
    for j in horn_var:
        for k in plag_var:
            for l in mag_var:
                for m in ap_var:
                    count = count+1
                    if abs((i+j+k+l+m)-1.0)<0.002:
                        poss_comb.append([i,j,k,l,m])
print(count)

```

I've been looking into mpi4py as a means to speed up my for-loop, but I am not sure how to apply it to my example. Does anyone have any suggestions?

Thanks!

Zack Eriksen