Evaluating a function parallely using itertools product

I have to evaluate a function (let's call it my_func()) that takes 8 parameters as input and returns a scalar upon different matrix computations. Since I don't have any constrains on my_func(), I have no choice but to brute force all possibilities which amounts to 8^8 = 16777216. I started off using itertools's product functions and passed the generated sequences to the my_func() sequentially. Kindly take a look at the sample of my code below,

So far.....

from itertools import product
import numpy as np

def my_func(a,b,c,d,e,f,g,h): #Function that I have to evaluate
    #do some matrix computations and return some scalar q# 
    return q

def Evaluate():
    Range = [x for x in np.arange(0,3.60,0.5)] # A list that contains 8 elements
    it = itertools.product(Range, repeat=8) #Generate all possiblities
    Max = 0
    for i in it:
        Sum = my_func(*i)
        if Sum > Max:
            Max = present
    return Max

Result = Evaluate() #Output

Pitfalls...

Unfortunately, executing the above code sequentially takes ages to produce the output. This is because my_func is quite heavy. I have no choice but to somehow parallelize this code so that I can take advantage of multiple processors available to run my code.

Questions:

Since itertools.product is a generator I cannot parallelize this to evaluate my_func() simultaneously for different sets of parameters. Is there any way I can parallelize the code? if no is the asnswer, should I abondon the idea of using itertools and try someting else?

Kindly help me find a solution.

Thanks for suggesting any ideas.

Cheers!!