OpenMP和Python [英] OpenMP and Python

问题描述

我有为共享内存机器(在C和FORTRAN中)编码OpenMP的经验，可以执行简单的任务，例如矩阵加法，乘法等(仅看它如何与LAPACK竞争).我知道OpenMP足以执行简单的任务，而无需查看文档.

I have experience in coding OpenMP for Shared Memory machines (in both C and FORTRAN) to carry out simple tasks like matrix addition, multiplication etc. (Just to see how it competes with LAPACK). I know OpenMP enough to carry out simple tasks without the need to look at documentation.

最近，我转向项目的Python，除了绝对的基础知识之外，我对Python没有任何经验.

Recently, I shifted to Python for my projects and I don't have any experience with Python beyond the absolute basics.

我的问题是:

在Python中使用OpenMP的最简单方法是什么?最简单的说，我的意思是在程序员方面花费最少的工作(即使这是以增加系统时间为代价的)?

What is the easiest way to use OpenMP in Python? By easiest, I mean the one that takes least effort on the programmer side (even if it comes at the expense of added system time)?

我使用OpenMP的原因是因为串行代码可以转换为工作的并行代码，并且周围散布着几个!$OMP.实现 rough 并行化所需的时间非常短.有什么办法可以在Python中复制此功能?

The reason I use OpenMP is because a serial code can be converted to a working parallel code with a few !$OMPs scattered around. The time required to achieve a rough parallelization is fascinatingly small. Is there any way to replicate this feature in Python?

通过浏览SO，我可以找到:

From browsing around on SO, I can find:

• C扩展名
• StackLess Python

还有更多吗?哪个最符合我的问题?

Are there more? Which aligns best with my question?

推荐答案

由于GIL，没有必要在CPython中将线程用于CPU密集型任务.您需要进行多处理(示例)或使用在计算过程中释放GIL的C扩展，例如一些numpy函数，示例.

Due to GIL there is no point to use threads for CPU intensive tasks in CPython. You need either multiprocessing (example) or use C extensions that release GIL during computations e.g., some of numpy functions, example.

您可以轻松地在Cython中编写使用多个线程的C扩展，示例.

You could easily write C extensions that use multiple threads in Cython, example.

这篇关于OpenMP和Python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IT屋！