python计算密集型任务多进程最佳使用的核数

import time
from multiprocessing import Pool
from tqdm import tqdm
import pandas as pd
import matplotlib.pyplot as plt
def run(i):
    count=0
    for j in range(100000):
        count+=j*j
    return count
def test(cpu_count):
    main_start = time.time() #记录主进程开始的时间
    with Pool(cpu_count) as p:
            #results = p.map(self.run, range(params_list_len))
        results = list(p.map(run, range(1000)))
    main_end = time.time()  #记录主进程结束时间
    return main_end-main_start
cup_time_list=[]
for i in range(1,25):
    print(i)
    now_time=test(i)
    cup_time_list.append([i,now_time])
cpu_time=pd.DataFrame(cup_time_list)
plt.plot(list(cpu_time[0]),list(cpu_time[1]))
plt.title("the relationship between the cup_num and computing time consume",color='red') 
plt.show()