python - 使用多处理后的不同结果

Question

小伙伴们:

我是 python 多处理的新手。最近我的研究需要多次迭代计算。所以我尝试使用多处理来加速它。但是当我写一个小示例代码时，我发现我使用多处理得到的曲线和没有使用多处理得到的曲线是不同的。

多处理代码:

import random
import matplotlib.pyplot as plt
import math
import numpy as np
import multiprocessing as mp

class Classic:
    def __init__(self,position,type):
        assert type == 'A' or type == 'B'    
        self.position = position
        self.type = type
    def getposition(self):
        return self.position
    def gettype (self):
        return self.type
    def setposition(self,pos):
        self.position = pos
    def settype (self,t):
        self.type = t
def number_count(system):
    counter = 0
    for i in range(0,len(system)):
        if system[i] !=0:
            counter=counter+1
    return counter
def time_evolution(system_temp,steps):
    numberlist=np.zeros(steps)
    number = number_count(system_temp)
    for t in range(0,steps):
        for i in range(0,len(system_temp)):
            x = random.randint(0, len(system_temp)-2)
            if system_temp[x]!=0 and system_temp[x+1]!=0:
                p1 = system_temp[x]
                p2 = system_temp[x+1]
                p1_type = p1.gettype()
                p2_type = p2.gettype()
                exchange_check = random.randint(0,1)
                if p1_type == p2_type:
                    system_temp[x]=0
                    system_temp[x+1]=0
                    number = number-2
                elif exchange_check == 1:
                    type_temp = p1_type
                    p1.settype(p2_type)
                    p2.settype(type_temp)
            elif system_temp[x]!=0 and system_temp[x+1]==0:
                system_temp[x+1] = system_temp[x]
                system_temp[x] =0
            elif system_temp[x]==0 and system_temp[x+1]!=0:
                system_temp[x]=system_temp[x+1]
                system_temp[x+1]=0
        numberlist[t]=numberlist[t]+number
    return numberlist

if __name__ =='__main__':
    pool = mp.Pool(8)
    size = 10000
    system_init = [0]*size
    particle_num = 3000
    repeat = 20
    steps = 2000
    res=[]
    totalnum= np.zeros(steps)
    randomlist = random.sample(range(1,100*repeat),repeat)
    for i in range(0,particle_num):
        pos = random.randint(0,size-1)
        ran_num = random.randint (0,1)
        if ran_num == 0:
            temp_type = 'A'
        else:
            temp_type = 'B'
        if system_init[pos] ==0:
            system_init[pos] = Classic(pos,temp_type)
    for k in range(0, repeat):
        system_temp = system_init[:]
        random.seed(randomlist[k])
        res.append(pool.apply_async(time_evolution, args=(system_temp,steps,)))
    pool.close()
    pool.join()
    for count in range(0,len(res)):
        totalnum =totalnum+ np.array(res[count].get())
    time=np.linspace(1,steps+1,steps)
    time_sqrt=np.sqrt(8.0*math.pi*time)
    density =totalnum/(repeat*size)
    density_mod = np.multiply(time_sqrt,density)
    #plt.loglog(time,density_mod)
    #plt.savefig("modified_density_loglog.pdf")
    #plt.close()
    myfile=open('density_mod2.txt','w')
    for element in density_mod:
        myfile.write(str(element))
        myfile.write('\n')
    myfile.close()

没有多处理的代码是

import random
import matplotlib.pyplot as plt
import math
import numpy as np
class Classic:
    def __init__(self,position,type):
        assert type == 'A' or type == 'B'    
        self.position = position
        self.type = type
    def getposition(self):
        return self.position
    def gettype (self):
        return self.type
    def setposition(self,pos):
        self.position = pos
    def settype (self,t):
        self.type = t
def number_count(system):
    counter = 0
    for i in range(0,len(system)):
        if system[i] !=0:
            counter=counter+1
    return counter    

def time_evolution(system_temp,steps):
    numberlist=np.zeros(steps)
    number = number_count(system_temp)
    for t in range(0,steps):
        for i in range(0,len(system_temp)):
            x = random.randint(0, len(system_temp)-2)
            if system_temp[x]!=0 and system_temp[x+1]!=0:
                p1 = system_temp[x]
                p2 = system_temp[x+1]
                p1_type = p1.gettype()
                p2_type = p2.gettype()
                exchange_check = random.randint(0,1)
                if p1_type == p2_type:
                    system_temp[x]=0
                    system_temp[x+1]=0
                    number = number-2
                elif exchange_check == 1:
                    type_temp = p1_type
                    p1.settype(p2_type)
                    p2.settype(type_temp)
            elif system_temp[x]!=0 and system_temp[x+1]==0:
                system_temp[x+1] = system_temp[x]
                system_temp[x] =0
            elif system_temp[x]==0 and system_temp[x+1]!=0:
                system_temp[x]=system_temp[x+1]
                system_temp[x+1]=0
        numberlist[t]=numberlist[t]+number
    return numberlist

size = 10000
system_init = [0]*size
particle_num = 3000
repeat = 20
steps = 2000
res=[]
totalnum= np.zeros(steps)
randomlist = random.sample(range(1,100*repeat),repeat)
for i in range(0,particle_num):
    pos = random.randint(0,size-1)
    ran_num = random.randint (0,1)
    if ran_num == 0:
        temp_type = 'A'
    else:
        temp_type = 'B'
    if system_init[pos] ==0:
        system_init[pos] = Classic(pos,temp_type)
for k in range(0, repeat):
    system_temp = system_init[:]
    random.seed(randomlist[k])
    res.append(time_evolution(system_temp,steps))

for count in range(0,len(res)):
    totalnum +=res[count]
time=np.linspace(1,steps+1,steps)
time_sqrt=np.sqrt(8.0*math.pi*time)
density =totalnum/(repeat*size)
density_mod = np.multiply(time_sqrt,density)
myfile=open('density_mod3.txt','w')
for element in density_mod:
    myfile.write(str(element))
    myfile.write('\n')
myfile.close()

结果显示为

enter image description here

蓝色曲线是多处理的结果，橙色曲线是没有多处理的结果。我不确定为什么会这样。如何解决？

Answer 1

我的猜测是您没有正确初始化随机数生成器。您必须在生成的进程“内部”执行此操作。

检查以下简单示例:

import random
import multiprocessing as mp

def rand_test_1():
    print(random.randint(0, 100))
    return None

def rand_test_2(seed):
    random.seed(seed)
    print(random.randint(0, 100))
    return None

if __name__ == '__main__':

    repeat = 3
    randomlist = random.sample(range(1, 100 * repeat), repeat)

    print('Classic:')
    for k in range(repeat):
        random.seed(randomlist[k])
        rand_test_1()

    print('\nMultiprocessing version 1:')
    with mp.Pool() as pool:
        for k in range(repeat):
            random.seed(randomlist[k])
            pool.apply_async(rand_test_1, args=tuple())
        pool.close()
        pool.join()

    print('\nMultiprocessing version 2:')
    with mp.Pool() as pool:
        for k in range(repeat):
            pool.apply_async(rand_test_2, args=(randomlist[k],))
        pool.close()
        pool.join()

结果如下:

Classic:
32
78
6

Multiprocessing version 1:
84
43
90

Multiprocessing version 2:
32
78
6

您正在使用多处理版本 1，我认为您应该使用版本 2。

与您的问题无关的另一点:我的印象是使用 .map/.starmap 可能是个好主意(参见 here )而不是 .apply_async:

    ...
    with mp.Pool() as pool:
        res = list(pool.map(rand_test_2, randomlist))