Numpy 多处理时间的增加

我定义了一个多进程脚本来改进图像分析。它似乎工作得很好，但我试着做了几个测试，以确定最佳进程数。 它包括改变这个过程的数量。由于存在一些分散，我添加了一个循环，以便重复我的测试一百次。 但在这个过程中，时间显著增加。我的问题的根源是什么？我必须刷新内存吗？但它似乎没有饱和

我的一段代码：

from multiprocessing import Process, current_process
import multiprocessing 
import glob as glob
import matplotlib.pyplot as plt
from skimage import io
import time
import sys
import numpy as np
import numpy.ma as ma
import gc
import os
from PIL import Image
from skimage import exposure
import cv2 


Path_input = "E:\\test\\raw\\"
Path_output = "E:\\test\\"

Img_list = glob.glob((Path_input + 'Test_*.tif' ))[:]
size_y,size_x = io.imread(Img_list[0]).shape

#Function for the multi process
def Ajustement(x):
    #image reading
    img = plt.imread(Img_list[x])

    #create a CLAHE object
    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
    cl1 = clahe.apply(img_rescale.astype(np.uint16))    

    cv2.imwrite(Path_output+ '\\Ajusted' + "%05d" % x + '.tif',cl1) 

    return 'Ajustement OK!'


#create strings list of process
cpu_max = 10
list = ['Process_']*cpu_max
list_process =[]
counter = 1
for u in list:
    list_process.append(list[counter-1]+np.str(counter))
    counter = counter+1

get_timer = time.clock if sys.platform == "win32" else time.time

time_store = []
time_process = []

if __name__ == '__main__':
    range_adjusted = np.arange(0,len(Img_list),cpu_max)
    m=0
    for m in range(0,100,1): #loop for obtain a mean time for the process
        print m
        timer = get_timer()  # time measuring starts now
        for i in range_adjusted:
            o = 0
            for item in list_process[:cpu_max]: #process creation
                globals()[item] = Process(name ='worker1', target=Normalization_and_ajustement, args=(i+o,))
                o=o+1               
            o = 0
            for item in list_process[:cpu_max]: #process start
                globals()[item].start() 
                o=o+1               
            o = 0
            for item in list_process[:cpu_max]: #process join
                globals()[item].join()  
                o=o+1   

            if i == range_adjusted.max():
                print("Normalization and Equalization finished")

                timer = get_timer() - timer  # get delta time as soon as it finishes
                time_store.append(timer)
                time_process.append(timer/cpu_max)

                np.savetxt(Path_output + 'time_tot_normalization.txt',time_store)
                np.savetxt(Path_output + 'time_process_normalization.txt',time_process)

                print("\tTotal: {:.2f} seconds".format(timer))
                print("\tAvg. per process: {:.2f} seconds".format(timer/cpu_max))
        m=m+1

---

我想这是因为内存泄漏。实际上，我在每个循环之后添加了

gc.collect（）

命令，问题就解决了