python multiprocessing vs threading for cpu bound work on wi

2023-03-14Python开发问题
5

本文介绍了python multiprocessing vs threading for cpu bound work on windows and linux的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着跟版网的小编来一起学习吧!

问题描述

所以我敲了一些测试代码,看看多处理模块在 cpu 绑定工作上与线程相比如何扩展.在 linux 上,我得到了预期的性能提升:

So I knocked up some test code to see how the multiprocessing module would scale on cpu bound work compared to threading. On linux I get the performance increase that I'd expect:

linux (dual quad core xeon):
serialrun took 1192.319 ms
parallelrun took 346.727 ms
threadedrun took 2108.172 ms

我的双核 macbook pro 显示相同的行为:

My dual core macbook pro shows the same behavior:

osx (dual core macbook pro)
serialrun took 2026.995 ms
parallelrun took 1288.723 ms
threadedrun took 5314.822 ms

然后我在一台windows机器上试了一下,得到了一些非常不同的结果.

I then went and tried it on a windows machine and got some very different results.

windows (i7 920):
serialrun took 1043.000 ms
parallelrun took 3237.000 ms
threadedrun took 2343.000 ms

为什么,为什么,Windows 上的多处理方法这么慢?

Why oh why, is the multiprocessing approach so much slower on windows?

这是测试代码:

#!/usr/bin/env python

import multiprocessing
import threading
import time

def print_timing(func):
    def wrapper(*arg):
        t1 = time.time()
        res = func(*arg)
        t2 = time.time()
        print '%s took %0.3f ms' % (func.func_name, (t2-t1)*1000.0)
        return res
    return wrapper


def counter():
    for i in xrange(1000000):
        pass

@print_timing
def serialrun(x):
    for i in xrange(x):
        counter()

@print_timing
def parallelrun(x):
    proclist = []
    for i in xrange(x):
        p = multiprocessing.Process(target=counter)
        proclist.append(p)
        p.start()

    for i in proclist:
        i.join()

@print_timing
def threadedrun(x):
    threadlist = []
    for i in xrange(x):
        t = threading.Thread(target=counter)
        threadlist.append(t)
        t.start()

    for i in threadlist:
        i.join()

def main():
    serialrun(50)
    parallelrun(50)
    threadedrun(50)

if __name__ == '__main__':
    main()

推荐答案

进程在 UNIX 变体下更加轻量级.Windows 进程很繁重,需要更多时间才能启动.线程是在 Windows 上进行多处理的推荐方式.

Processes are much more lightweight under UNIX variants. Windows processes are heavy and take much more time to start up. Threads are the recommended way of doing multiprocessing on windows.

这篇关于python multiprocessing vs threading for cpu bound work on windows and linux的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持跟版网!

The End

相关推荐

在xarray中按单个维度的多个坐标分组
groupby multiple coords along a single dimension in xarray(在xarray中按单个维度的多个坐标分组)...
2024-08-22 Python开发问题
15

Pandas中的GROUP BY AND SUM不丢失列
Group by and Sum in Pandas without losing columns(Pandas中的GROUP BY AND SUM不丢失列)...
2024-08-22 Python开发问题
17

pandas 有从特定日期开始的按月分组的方式吗?
Is there a way of group by month in Pandas starting at specific day number?( pandas 有从特定日期开始的按月分组的方式吗?)...
2024-08-22 Python开发问题
10

GROUP BY+新列+基于条件的前一行抓取值
Group by + New Column + Grab value former row based on conditionals(GROUP BY+新列+基于条件的前一行抓取值)...
2024-08-22 Python开发问题
18

PANDA中的Groupby算法和插值算法
Groupby and interpolate in Pandas(PANDA中的Groupby算法和插值算法)...
2024-08-22 Python开发问题
11

PANAS-基于列对行进行分组,并将NaN替换为非空值
Pandas - Group Rows based on a column and replace NaN with non-null values(PANAS-基于列对行进行分组,并将NaN替换为非空值)...
2024-08-22 Python开发问题
10