Python - how to put a combination of one that always goes in a map and one that transforms

from multiprocessing import pool
dist_result = []

with Pool() as p:
    dist_result.append(p.map(dist_cal, !!!!(total_point, range(100) !!!!))

The part where the exclamation mark is stacked is the part that I don't know. The code you want to implement is as follows:

By default, total_point is a set of millions of 100-dimensional intasers. [Example] [1, 2, 3...100] , [-1,2,3...], [12,4,2,...] ...]

dist_cal is a function. Find the Euclidean distance between the internal points. However, if you add dimension, out of the dots, We will only reflect the dimension you put in

import itertools
def dist_cal(points_list, dim):

    result = 0
    for combi in itertools.combinations(points_list, 2):
        tmp = 0
        for i in range(dim):
            tmp += (combi[0][i]-combi[1][i])**2
        result += tmp**0.5

    return result

from 0 to 100 dimensions As we increase the dimension, we want to observe the change in the distance between the points, and the goal is to parallelize it because of the large amount of computation.

Back to the code you want to ask

from multiprocessing import pool
dist_result = []

with Pool() as p:
    dist_result.append(p.map(dist_cal, !!!!(total_point, range(100) !!!!))

Total_point is the default input that does not change for all parallel operations. But it just wants to run 0 to 100 differently, to run this in parallel When you put it in p.map(), you put a function and its factors separately.

When generating these factors,

This is because I don't know how to generate it in the form of (fixed total points, numbers from 1 to 100 that keep changing).

Please.

==============================

def genero(po, N):
    count = 0
    for i in range(N):
        yield po, i

with Pool() as p:
    dist_result.append(p.map(dist_cal, genero(total_point, 10) ))

I'm approaching it, but I don't know if it's the right approach. Why not;;;;