Python解释器最多占用我130％的CPU.那怎么可能? [英] Python interpreters uses up to 130% of my CPU. How is that possible?

问题描述

我目前正在使用python进行一些 I/O密集型负载测试.我的程序所做的就是尽可能快地向目标服务器发送 HTTP 请求.

I am currently doing some I/O intensive load-testing using python. All my program does is to send HTTP requests as fast as possible to my target server.

要管理此问题，我使用最多20个线程 ，因为我本质上受I/O和远程服务器限制的约束.

To manage this, I use up to 20 threads as I'm essentially bound to I/O and remote server limitations.

根据顶部"， CPython 在我的双核计算机上使用的峰值为130％CPU .

According to 'top', CPython uses a peak of 130% CPU on my dual core computer.

那怎么可能?我以为GIL可以防止这种情况发生?还是Linux计算"每个应用程序消耗的资源的方式?

How is that possible ? I thought the GIL prevented this ? Or is it the way Linux 'counts' the resources consumed by each applications ?

推荐答案

前100％是指单个核心.在双核计算机上，您最多可以使用200％.

100 percent in top refer to a single core. On a dual-core machine, you have up to 200 per cent available.

单个单线程进程只能使用单个内核，因此限制为100％.由于您的进程有多个线程，因此没有什么可以阻止它同时使用两个内核.

A single single-threaded process can only make use of a single core, so it is limited to 100 percent. Since your process has several threads, nothing is stopping it from making use of both cores.

GIL仅阻止纯Python代码并发执行.许多库调用(包括大多数I/O东西)都会释放GIL，因此这里也没有问题.与Internet上的许多FUD相反，GIL很少会降低实际性能，如果这样做，通常比使用线程有更好的解决方案.

The GIL only prevents pure-Python code from being executed concurrently. Many library calls (including most I/O stuff) release the GIL, so no problem here as well. Contrary to much of the FUD on the internet, the GIL rarely reduces real-world performance, and if it does, there are usually better solutions to the problem than using threads.

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