Table of Contents
Introduction
Understanding Parallel Processing
Multiple threads in Python
Example
Output
Multiprocessing in Python
Python Asynchronous Programming
Example Domain
Choose the right method
in conclusion
Home Backend Development Python Tutorial Parallel processing in Python

Parallel processing in Python

Sep 11, 2023 pm 11:49 PM
python Multithreading parallel processing

Parallel processing in Python

Introduction

In today's fast-paced digital environment, it is critical for developers and data scientists to efficiently complete computationally difficult tasks. Fortunately, Python offers powerful parallel processing capabilities due to its adaptability and broad ecosystem. We can achieve substantial performance improvements by breaking down difficult problems into smaller, more manageable activities and working on them simultaneously.

Python’s parallel processing capabilities allow us to leverage available computer resources to perform activities such as web scraping, scientific simulations, and data analysis faster and more efficiently. In this article, we will start a journey through parallel processing in Python. We'll examine a number of methods, including multiprocessing, asynchronous programming, and multithreading, and learn how to use them effectively to bypass performance roadblocks in your system. Join us as we realize the full power of parallel processing in Python and reach new heights of performance and productivity.

Understanding Parallel Processing

Splitting a job into smaller subtasks and running them simultaneously on multiple processors or cores is called parallel processing. Parallel processing can significantly reduce the overall execution time of a program by efficiently utilizing available computing resources. Asynchronous programming, multiprocessing, and multithreading are just a few of the parallel processing methods Python offers.

Multiple threads in Python

Using the multi-threading method, many threads run simultaneously in the same process and share the same memory. Multithreading can be easily implemented using Python's threading module. However, using multithreading in Python may not have a speedup effect on CPU-intensive operations because the Global Interpreter Lock (GIL) only allows one thread to execute Python bytecode at the same time. However, multithreading can be useful for I/O-intensive tasks because it allows threads to run other operations while waiting for I/O operations to complete.

Let’s look at an example of using multi-threading to download multiple web pages:

Example

import threading import requests 
 
def download_page(url): 
    response = requests.get(url)    
print(f"Downloaded {url}") 
 
urls = [ 
    "https://example.com", 
    "https://google.com", 
    "https://openai.com" 
] 
 
threads = [] 
 for url in 
 urls: 
    thread = threading.Thread(target=download_page,
args=(url,))     thread.start()    threads.append(thread) 
 
for thread in threads: 
    thread.join() 
Copy after login

Output

Downloaded https://example.com 
Downloaded https://google.com 
Downloaded https://openai.com 
Copy after login

Since the above code snippet can do multiple downloads at the same time, this code snippet downloads each URL in its own thread. The join() function ensures that the main thread waits for each thread to complete before continuing.

Multiprocessing in Python

Multi-process corresponds to multi-threading. By using multiple processes, each process has its own memory space, providing true parallelism. Python's multiprocessing module provides a high-level interface to implement multiple processes. Multiprocessing is suitable for CPU-intensive tasks because each process runs in an independent Python interpreter, avoiding GIL multithreading limitations.

Multiple processes are used in the code below. Once the pool class has spawned a set of worker processes, the map() method distributes the burden to the available processes. A result list is a collection of results.

Consider the following example, in which we use multiple processes to calculate the square of each integer in a list:

Example

import multiprocessing 
 
def square(number):    
return number ** 2 
 
numbers = [1, 2, 3, 4, 5] 
 
with multiprocessing.Pool() as pool: 
    results = pool.map(square, numbers) 
 
print(results) 
Copy after login

Output

[1, 4, 9, 16, 25] 
Copy after login

Python Asynchronous Programming

By taking advantage of non-blocking operations, asynchronous programming enables efficient execution of I/O-intensive processes. Thanks to the asyncio package, Python can create asynchronous code using coroutines, event loops, and futures. As online applications and APIs become more popular, asynchronous programming becomes increasingly important.

The fetch_page() coroutine in the code example below uses aiohttp to asynchronously obtain web pages. The main() method generates a list of jobs and then uses asyncio.gather() to execute these jobs simultaneously. To wait for a task to complete and receive the results, use the await keyword.

Let's look at an example of using asyncio and aiohttp to asynchronously obtain multiple web pages:

Example

import asyncio 
import aiohttp 
 
async def fetch_page(url):     async with aiohttp.ClientSession() as session:         async with session.get(url) as response: 
            return await response.text() 
 
async def main(): 
    urls = [ 
        "https://example.com", 
        "https://google.com", 
        "https://openai.com" 
    ] 
 
    tasks = [fetch_page(url) for url in urls]     pages = await asyncio.gather(*tasks)     
print(pages) 
 
asyncio.run(main()) 
Copy after login

Output

['<!doctype html>\n<html>\n<head>\n    <title>Example Domain</title>\n\n    <meta 
charset="utf-8" />\n    <meta http-equiv="Content-type"content="text/html; charset=utf-8" />\n    <meta name="viewport" content="width=device-width, initialscale=1" />\n    <style type="text/css">\n    body {\n        background-color: #f0f0f2;\n  margin: 0;\n        padding: 0;\n        font-family: "Open Sans", "Helvetica Neue", Helvetica, Arial, sans-serif;\n        \n    }\n    div {\n        width: 600px;\n        margin: 5em auto;\n  padding: 50px;\n        background-color: #fff;\n        border-radius: 1em;\n    }\n    a:link, a:visited {\n        color: #38488f;\n        text-decoration: none;\n    }\n    @media (maxwidth: 700px) {\n        body {\n            background-color: #fff;\n        }\n        div {\n  width: auto;\n            margin: 0 auto;\n            border-radius: 0;\n            padding: 1em;\n        }\n    }\n    </style>    \n</head>\n\n<body>\n<div>\n    <h1 id="Example-Domain">Example Domain</h1>\n    <p>This domain is for use in illustrative examples in documents. You may use this\n    domain in literature without prior coordination or asking for permission.</p>\n    <p><a href="https://www.iana.org/domains/example">More information...</a></p>\n</div>\n</body>\n</html>', '<!doctype html><html itemscope="" itemtype="http://schema.org/WebPage" lang="en"><head><meta content="Search the world's information, including webpages, images, videos and more. Google has many special features to help you find exactly what you're looking for." name="description"><meta content="noodp" name="robots"><meta content="text/html; charset=UTF-8" http-equiv="Content-Type"><meta content="/logos/doodles/2021/mom-
and-dad-6116550989716480.2-law.gif" itemprop="image"><link href="/logos/doodles/2021/mom-and-dad-6116550989716480.2-law.gif" rel="icon" type="image/gif"><title>Google</title><script nonce="sJwM0Ptp5a/whzxPtTD8Yw==">(function(){window.google={kEI:'cmKgYY37A7 K09QPhzKuACw',kEXPI:'1354557,1354612,1354620,1354954,1355090,1355493,13556
83,3700267,4029815,4031109,4032677,4036527,4038022,4043492,4045841,4048347,4
048490,4052469,4055589,4056520,4057177,4057696,4060329,4060798,4061854,4062 531,4064696,406 '
Copy after login

Choose the right method

Python's parallel processing techniques vary depending on the specific circumstances of the task. Here are some guidelines to help you make an informed decision:

For I/O-intensive activities, where most of the execution time is spent waiting for input/output operations, multithreading is appropriate. It is suitable for tasks such as downloading files, using APIs, and manipulating files. Due to Python's Global Interpreter Lock (GIL), multithreading may not significantly speed up CPU-intensive activities.

On the other hand, multi-processing is suitable for CPU-bound tasks involving intensive calculations. It achieves true parallelism by utilizing multiple processes, each with its own memory space, bypassing the limitations of the GIL. However, it incurs additional overhead in terms of memory consumption and inter-process communication.

Asynchronous programming performed using libraries such as asyncio is useful for I/O-intensive activities involving network operations. It utilizes non-blocking I/O operations so that jobs can continue without waiting for each operation to complete. This approach efficiently manages multiple concurrent connections, making it suitable for web server development, Web API interactions, and web scraping. Asynchronous programming minimizes wait times for I/O operations, ensuring responsiveness and scalability.

in conclusion

Python's parallel processing capabilities provide opportunities to increase efficiency in tasks that require complex calculations. Whether you choose to use multithreading, multiprocessing, or asynchronous programming, Python provides the necessary tools and modules to take advantage of concurrency effectively. By understanding the nature of the activity and choosing the appropriate technology, you can maximize the benefits of parallel processing and reduce execution time. So, keep exploring and taking full advantage of Python’s parallelism to create faster, more efficient applications.

The above is the detailed content of Parallel processing in Python. For more information, please follow other related articles on the PHP Chinese website!

Statement of this Website
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

Video Face Swap

Video Face Swap

Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Tools

Notepad++7.3.1

Notepad++7.3.1

Easy-to-use and free code editor

SublimeText3 Chinese version

SublimeText3 Chinese version

Chinese version, very easy to use

Zend Studio 13.0.1

Zend Studio 13.0.1

Powerful PHP integrated development environment

Dreamweaver CS6

Dreamweaver CS6

Visual web development tools

SublimeText3 Mac version

SublimeText3 Mac version

God-level code editing software (SublimeText3)

Is the vscode extension malicious? Is the vscode extension malicious? Apr 15, 2025 pm 07:57 PM

VS Code extensions pose malicious risks, such as hiding malicious code, exploiting vulnerabilities, and masturbating as legitimate extensions. Methods to identify malicious extensions include: checking publishers, reading comments, checking code, and installing with caution. Security measures also include: security awareness, good habits, regular updates and antivirus software.

How to run programs in terminal vscode How to run programs in terminal vscode Apr 15, 2025 pm 06:42 PM

In VS Code, you can run the program in the terminal through the following steps: Prepare the code and open the integrated terminal to ensure that the code directory is consistent with the terminal working directory. Select the run command according to the programming language (such as Python's python your_file_name.py) to check whether it runs successfully and resolve errors. Use the debugger to improve debugging efficiency.

Can vs code run in Windows 8 Can vs code run in Windows 8 Apr 15, 2025 pm 07:24 PM

VS Code can run on Windows 8, but the experience may not be great. First make sure the system has been updated to the latest patch, then download the VS Code installation package that matches the system architecture and install it as prompted. After installation, be aware that some extensions may be incompatible with Windows 8 and need to look for alternative extensions or use newer Windows systems in a virtual machine. Install the necessary extensions to check whether they work properly. Although VS Code is feasible on Windows 8, it is recommended to upgrade to a newer Windows system for a better development experience and security.

Can visual studio code be used in python Can visual studio code be used in python Apr 15, 2025 pm 08:18 PM

VS Code can be used to write Python and provides many features that make it an ideal tool for developing Python applications. It allows users to: install Python extensions to get functions such as code completion, syntax highlighting, and debugging. Use the debugger to track code step by step, find and fix errors. Integrate Git for version control. Use code formatting tools to maintain code consistency. Use the Linting tool to spot potential problems ahead of time.

Choosing Between PHP and Python: A Guide Choosing Between PHP and Python: A Guide Apr 18, 2025 am 12:24 AM

PHP is suitable for web development and rapid prototyping, and Python is suitable for data science and machine learning. 1.PHP is used for dynamic web development, with simple syntax and suitable for rapid development. 2. Python has concise syntax, is suitable for multiple fields, and has a strong library ecosystem.

Can vscode be used for mac Can vscode be used for mac Apr 15, 2025 pm 07:36 PM

VS Code is available on Mac. It has powerful extensions, Git integration, terminal and debugger, and also offers a wealth of setup options. However, for particularly large projects or highly professional development, VS Code may have performance or functional limitations.

Can vscode run ipynb Can vscode run ipynb Apr 15, 2025 pm 07:30 PM

The key to running Jupyter Notebook in VS Code is to ensure that the Python environment is properly configured, understand that the code execution order is consistent with the cell order, and be aware of large files or external libraries that may affect performance. The code completion and debugging functions provided by VS Code can greatly improve coding efficiency and reduce errors.

Golang vs. Python: Concurrency and Multithreading Golang vs. Python: Concurrency and Multithreading Apr 17, 2025 am 12:20 AM

Golang is more suitable for high concurrency tasks, while Python has more advantages in flexibility. 1.Golang efficiently handles concurrency through goroutine and channel. 2. Python relies on threading and asyncio, which is affected by GIL, but provides multiple concurrency methods. The choice should be based on specific needs.

See all articles