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我编写了一个 python 程序来并行化一堆矩阵乘法。以下代码将整个计算分配给四个独立的进程。（为了简化演示，我删除了收集结果的代码。）由于矩阵乘法需要重复数千次，因此四个进程保持活动状态，并且可以通过管道暂停/恢复。问题是当我只恢复一个进程时，它会很快完成（0.13s）。但是，当恢复更多进程时，每个进程所消耗的时间会增加。（两个 0.31 秒，三个 0.45 秒，四个 0.6 秒）。由于这四个进程是完全独立的，所以我真的不明白这里发生了什么。

import multiprocessing as mp
import numpy as np
import time

def worker(a,b,Ind):
    st = time.time()
    M = len(a)
    a = np.reshape(a,[1,M])
    NN = b.shape[1]
    result = np.zeros([NN,len(Ind)])
    # loop for delay points
    for ii in range(0,len(Ind)):
        temp = a.dot(b[Ind[ii]:Ind[ii]+M,:])
        result[:,ii] = np.abs(temp)**2

    print(f'Elapsed time {time.time()-st} for {len(Ind)} loops')
    return result

def missions(a,b,Ind,Bob):
    while True:
        # wait until receive something
        flag = Bob.recv()
        if flag==1:
            temp = worker(a,b,Ind)
            Bob.send(1)


if __name__=='__main__':
    N = 4
    M = 160
    procs = []
    pipes = []
    Ind0 = np.array(range(1600))*10
    a = np.random.random(1998)
    b = np.random.random((17988,700))+1j*np.random.random((17988,700))
    for ii in range(N):
        Ind_trace = np.array(range(0,M))+ii*M
        Ind_trace.astype(int)
        Ind = Ind0[Ind_trace]
        # add pipes
        Alice,Bob = mp.Pipe()
        pipes.append({'Alice':Alice,'Bob':Bob})
        # add process
        procs.append(mp.Process(target=missions,args=(a,b,Ind,pipes[ii]['Bob'])))
    
    for p in procs:
        p.start()
    
    # start process
    tasks = [0,1,2,3]
    #tasks = list([0])
    # start tasks
    for ii in tasks:
        pipes[ii]['Alice'].send(1) # send signal to Bob to start mission

    # check if all the tasks is finished
    while(True):
        if len(tasks)==0:
            break
        for ii in tasks:
            if pipes[ii]['Alice'].poll():
                if pipes[ii]['Alice'].recv()==1:  #finished
                    tasks.remove(ii)

    for p in procs:
        p.terminate()
        p.kill()
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