Externalising CPU computation from Python for multi core concurrency смотреть онлайн

In this tutorial, we will explore how to externalize CPU computation from Python in order to take full advantage of multi-core concurrency. Python is a high-level language that can be slow for CPU-bound tasks. To overcome this limitation, we will leverage external tools and libraries to perform computation-intensive tasks in a more efficient manner. We will also provide code examples to demonstrate the concepts discussed.
Before we start, make sure you have the following installed:
Python is known for its simplicity and ease of use, but it may not be the best choice for tasks that require heavy CPU computation. To make the most of multi-core CPUs, we can utilize various techniques and libraries to offload CPU-bound work from Python to external processes or libraries.
The multiprocessing library is a built-in Python library that allows you to create parallel processes. This is a good choice for tasks that can be parallelized easily, like running a function on multiple datasets.
The concurrent.futures module is another built-in Python library that provides a high-level interface for asynchronously executing functions using threads or processes.
For computationally intensive tasks, you can write your code in a language like C, C++, or Rust, which are compiled and offer better performance. You can then call these external programs from Python using the subprocess module.
Here's an example using C:
Python code to call the external C program:
In this tutorial, we explored techniques to externalize CPU computation from Python to leverage multi-core concurrency. Depending on your use case and programming preferences, you can choose between built-in Python libraries like multiprocessing and concurrent.futures, or external tools like C, C++, or Rust for performance-critical tasks. By optimizing your code in this way, you can make the most of your multi-core CPUs and significantly improve the performance of your CPU-bound computations.
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