Python 多进程调用 numpy 共轭期间的奇怪行为

所附的脚本针对不同大小的矩阵上的不同数量的并行进程计算numpy.congiate routine ，并记录相应的运行时间. 矩阵形状只在其第一维上变化(从1，64，64到256，64，64).共轭调用总是在1，64，64个子矩阵上进行，以确保正在处理的部分适合我系统上的L2缓存(每个内核256KB，在我的例子中L3缓存是25MB).运行该脚本将生成下图(具有略微不同的AX标签和 colored颜色 ).

正如您所看到的，从大约100、64、64的形状开始，运行时间取决于所使用的并行进程的数量.

What could be the cause of this ?
Or why is the dependence on the number of processes for matrices below (100,64,64) so low?
My main goal is to find a modification to this script such that the runtime becomes as independent as possible from the number of processes for matrices 'a' of arbitrary size.

In case of 20 Processes:
all 'a' matrices take at most: 20 * 16 * 256 * 64 * 64 Byte = 320MB
all 'b' sub matrices take at most: 20 * 16 * 1 * 64 * 64 Byte = 1.25MB
So all sub matrices fit simultaneously in L3 cache as well as individually in the L2 cache per core of my CPU. I did only use physical cores no hyper-threading for these tests.

以下是脚本:

from multiprocessing import Process, Queue
import time
import numpy as np
import os
from matplotlib import pyplot as plt
os.environ['OPENBLAS_NUM_THREADS'] = '1'
os.environ['MKL_NUM_THREADS'] = '1'

def f(q,size):
    a = np.random.rand(size,64,64) + 1.j*np.random.rand(size,64,64)
    start = time.time()
    n=a.shape[0]
    for i in range(20):
        for b in a:
            b.conj()
    duration = time.time()-start
    q.put(duration)

def speed_test(number_of_processes=1,size=1):
    number_of_processes = number_of_processes
    process_list=[]
    queue = Queue()
    #Start processes
    for p_id in range(number_of_processes):
        p = Process(target=f,args=(queue,size))
        process_list.append(p)
        p.start()
    #Wait until all processes are finished
    for p in process_list:
        p.join()

    output = []
    while queue.qsize() != 0:
        output.append(queue.get())
    return np.mean(output)

if __name__ == '__main__':
    processes=np.arange(1,20,3)
    data=[[] for i in processes]
    for p_id,p in enumerate(processes):
        for size_0 in range(1,257):
            data[p_id].append(speed_test(number_of_processes=p,size=size_0))

    fig,ax = plt.subplots()
    for d in data:
        ax.plot(d)
    ax.set_xlabel('Matrix Size: 1-256,64,64')
    ax.set_ylabel('Runtime in seconds')

    fig.savefig('result.png')