Java自学者论坛

 找回密码
 立即注册

手机号码,快捷登录

恭喜Java自学者论坛(https://www.javazxz.com)已经为数万Java学习者服务超过8年了!积累会员资料超过10000G+
成为本站VIP会员,下载本站10000G+会员资源,会员资料板块,购买链接:点击进入购买VIP会员

JAVA高级面试进阶训练营视频教程

Java架构师系统进阶VIP课程

分布式高可用全栈开发微服务教程Go语言视频零基础入门到精通Java架构师3期(课件+源码)
Java开发全终端实战租房项目视频教程SpringBoot2.X入门到高级使用教程大数据培训第六期全套视频教程深度学习(CNN RNN GAN)算法原理Java亿级流量电商系统视频教程
互联网架构师视频教程年薪50万Spark2.0从入门到精通年薪50万!人工智能学习路线教程年薪50万大数据入门到精通学习路线年薪50万机器学习入门到精通教程
仿小米商城类app和小程序视频教程深度学习数据分析基础到实战最新黑马javaEE2.1就业课程从 0到JVM实战高手教程MySQL入门到精通教程
查看: 1229|回复: 0

python 导入numpy 导致多进程绑定同一个CPU问题解决方法

[复制链接]
  • TA的每日心情
    奋斗
    2024-11-24 15:47
  • 签到天数: 804 天

    [LV.10]以坛为家III

    2053

    主题

    2111

    帖子

    72万

    积分

    管理员

    Rank: 9Rank: 9Rank: 9

    积分
    726782
    发表于 2021-7-21 09:54:15 | 显示全部楼层 |阅读模式

    python 如果有导入numpy模块的import语句,会导致默认将多进程程序的每个进程都绑定到同一个CPU core上,

    失去了多进程在多核CPU上的性能优越性,这和CPU affinity(CPU亲和性)有关,解决办法:

    导入affinity包,执行:

    affinity.set_process_affinity_mask(0,2**multiprocessing.cpu_count()-1)

    以下是英文文档原文,供参考:

     

    Python refuses to use multiple cores – solution

    I was trying to get parallel Python to work and I noticed that if I run two Python scripts simultaneously – say, in two different terminals – they use the same core. Hence, I get no speedup from multiprocessing/parallel Python. After some searching around, I found out that in some circumstances importing numpy causes Python to stick all computations in one core. This is an issue with CPU affinity, and apparently it only happens for some mixtures of Numpy and BLAS libraries – other packages may cause the CPU affinity issue as well.

    There’s a package called affinity (Linux only AFAIK) that lets you set and get CPU affinity. Download it, run python setup.py install, and run this in Python or ipython:

    1
    2
    3
    4
    In [ 1 ]: import affinity
     
    In [ 2 ]: affinity.get_process_affinity_mask( 0 )
    Out[ 2 ]: 63

    This is good: 63 is a bitmask corresponding to 111111 – meaning all 6 cores are available to Python. Now running this, I get:

    1
    2
    3
    4
    In [ 4 ]: import numpy as np
     
    In [ 5 ]: affinity.get_process_affinity_mask( 0 )
    Out[ 5 ]: 1

    So now only one core is available to Python. The solution is simply to set the CPU affinity appropriately after import numpy, for instance:

    1
    2
    3
    4
    5
    import numpy as np
    import affinity
    import multiprocessing
     
    affinity.set_process_affinity_mask( 0 , 2 * * multiprocessing.cpu_count() - 1 )

     

    哎...今天够累的,签到来了1...
    回复

    使用道具 举报

    您需要登录后才可以回帖 登录 | 立即注册

    本版积分规则

    QQ|手机版|小黑屋|Java自学者论坛 ( 声明:本站文章及资料整理自互联网,用于Java自学者交流学习使用,对资料版权不负任何法律责任,若有侵权请及时联系客服屏蔽删除 )

    GMT+8, 2024-12-22 10:43 , Processed in 0.061029 second(s), 29 queries .

    Powered by Discuz! X3.4

    Copyright © 2001-2021, Tencent Cloud.

    快速回复 返回顶部 返回列表