Python

import multiprocessing
import numpy as np
import random
import os

def one_experiment(seed=0):
    # set seed
    random.seed(seed)
    os.environ['PYTHONHASHSEED'] = str(seed)
    np.random.seed(seed)

    return np.random.normal(0, 1, 1)

if __name__ == '__main__':
    experiment_num = 5
    core_num = 2
    with multiprocessing.Pool(processes=core_num) as pool:
        results = pool.map(one_experiment, list(range(experiment_num)))
    print(results)