日韩性视频-久久久蜜桃-www中文字幕-在线中文字幕av-亚洲欧美一区二区三区四区-撸久久-香蕉视频一区-久久无码精品丰满人妻-国产高潮av-激情福利社-日韩av网址大全-国产精品久久999-日本五十路在线-性欧美在线-久久99精品波多结衣一区-男女午夜免费视频-黑人极品ⅴideos精品欧美棵-人人妻人人澡人人爽精品欧美一区-日韩一区在线看-欧美a级在线免费观看

歡迎訪問 生活随笔!

生活随笔

當(dāng)前位置: 首頁 > 编程语言 > python >内容正文

python

python和c混合编程 gil,如何在python中使用C扩展来解决GIL

發(fā)布時(shí)間:2025/3/8 python 15 豆豆
生活随笔 收集整理的這篇文章主要介紹了 python和c混合编程 gil,如何在python中使用C扩展来解决GIL 小編覺得挺不錯(cuò)的,現(xiàn)在分享給大家,幫大家做個(gè)參考.

I want to run a cpu intensive program in Python across multiple cores and am trying to figure out how to write C extensions to do this. Are there any code samples or tutorials on this?

解決方案

You can already break a Python program into multiple processes. The OS will already allocate your processes across all the cores.

Do this.

python part1.py | python part2.py | python part3.py | ... etc.

The OS will assure that part uses as many resources as possible. You can trivially pass information along this pipeline by using cPickle on sys.stdin and sys.stdout.

Without too much work, this can often lead to dramatic speedups.

Yes -- to the haterz -- it's possible to construct an algorithm so tortured that it may not be sped up much. However, this often yields huge benefits for minimal work.

And.

The restructuring for this purpose will exactly match the restructuring required to maximize thread concurrency. So. Start with shared-nothing process parallelism until you can prove that sharing more data would help, then move to the more complex shared-everything thread parallelism.

總結(jié)

以上是生活随笔為你收集整理的python和c混合编程 gil,如何在python中使用C扩展来解决GIL的全部內(nèi)容,希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網(wǎng)站內(nèi)容還不錯(cuò),歡迎將生活随笔推薦給好友。