3 Parallelization questions

[pei-h]

from pymoo.core.problem import ElementwiseProblem
import multiprocessing
from pymoo.algorithms.soo.nonconvex.ga import GA
from pymoo.optimize import minimize
from pymoo.core.problem import StarmapParallelization
import numpy as np

class MyProblem(ElementwiseProblem):

def __init__(self, **kwargs):
    super().__init__(n_var=10000, n_obj=1, n_ieq_constr=0, xl=-500, xu=500, **kwargs)

def _evaluate(self, x, out, *args, **kwargs):
     out["F"] = (x ** 2).sum()

if name == 'main':

n_proccess = 8
pool = multiprocessing.Pool(n_proccess)
runner = StarmapParallelization(pool.starmap)

problem = MyProblem(elementwise_runner=runner)

res = minimize(problem, GA(), termination=("n_gen", 200), seed=1)
print('Threads:', res.exec_time)

pool.close()

I only slightly modified the example from the official tutorial — specifically, I increased n_var and xu, and decreased xl. Here are my questions:
(1) According to "top" from terminal, only one PID reaches 100% CPU usage, while the other 7 remain below 1%. Why is that?
(2) Changing n_proccess from 1 to 8 doesn’t make a significant performance difference. Why?
(3) Do vectorized matrix operations offer similar performance compared to using threads or processes?