Python 3:Pool 是否保持传递给 map 的原始数据顺序?

问题描述

我编写了一个小脚本来在 4 个线程之间分配工作负载并测试结果是否保持有序(相对于输入的顺序):

I have written a little script to distribute workload between 4 threads and to test whether the results stay ordered (in respect to the order of the input):

from multiprocessing import Pool
import numpy as np
import time
import random


rows = 16
columns = 1000000

vals = np.arange(rows * columns, dtype=np.int32).reshape(rows, columns)

def worker(arr):
    time.sleep(random.random())        # let the process sleep a random
    for idx in np.ndindex(arr.shape):  # amount of time to ensure that
        arr[idx] += 1                  # the processes finish at different
                                       # time steps
    return arr

# create the threadpool
with Pool(4) as p:
    # schedule one map/worker for each row in the original data
    q = p.map(worker, [row for row in vals])

for idx, row in enumerate(q):
    print("[{:0>2}]: {: >8} - {: >8}".format(idx, row[0], row[-1]))

对我来说，这总是会导致:

For me this always results in:

[00]:        1 -  1000000
[01]:  1000001 -  2000000
[02]:  2000001 -  3000000
[03]:  3000001 -  4000000
[04]:  4000001 -  5000000
[05]:  5000001 -  6000000
[06]:  6000001 -  7000000
[07]:  7000001 -  8000000
[08]:  8000001 -  9000000
[09]:  9000001 - 10000000
[10]: 10000001 - 11000000
[11]: 11000001 - 12000000
[12]: 12000001 - 13000000
[13]: 13000001 - 14000000
[14]: 14000001 - 15000000
[15]: 15000001 - 16000000

问题:那么，Pool在q<中存储每个map函数的结果时，是否真的保持原始输入的顺序?/代码>?

Question: So, does Pool really keep the original input's order when storing the results of each map function in q?

旁注:我问这个，因为我需要一种简单的方法来并行处理多个工人的工作.在某些情况下，排序无关紧要.但是，在某些情况下(如 q 中的结果)必须以原始顺序返回，因为我使用了一个依赖于有序数据的附加 reduce 函数.

Sidenote: I am asking this, because I need an easy way to parallelize work over several workers. In some cases the ordering is irrelevant. However, there are some cases where the results (like in q) have to be returned in the original order, because I'm using an additional reduce function that relies on ordered data.

性能:在我的机器上，这个操作比在单个进程上的正常执行快了大约 4 倍(正如预期的那样，因为我有 4 个内核).此外，所有 4 个内核在运行时均处于 100% 的使用率.

Performance: On my machine this operation is about 4 times faster (as expected, since I have 4 cores) than normal execution on a single process. Additionally, all 4 cores are at 100% usage during the runtime.

问题描述

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