Complete Guide to Python Parallel Processing | Efficient Implementation Methods and Applications

By 佐川 直弘 | Naohiro Sagawa (運営者)

※記事内に広告を含みます。Amazonアソシエイトとして適格販売により収入を得ています。

1. Introduction

The Importance of Parallel Processing in Python

Python is widely used as a simple and easy-to-use programming language. However, when dealing with complex data processing and computations, Python’s execution speed can sometimes be a bottleneck. To address this issue, parallel processing, which enables multiple tasks to run simultaneously, plays a crucial role. This article explores how parallel processing can be implemented in Python, covering basic techniques as well as practical use cases.

2. Methods of Parallel Processing in Python

Main Approaches to Parallel Processing

Python offers several ways to achieve parallel processing. The three main methods are:

1. Multithreading (threading module)
   Uses multiple threads to execute tasks concurrently. However, due to Python’s Global Interpreter Lock (GIL), its effectiveness is limited for CPU-intensive tasks.
2. Multiprocessing (multiprocessing module)
   Each process operates in an independent memory space, avoiding the limitations of GIL. This method enables true parallel execution, making it ideal for heavy computations and large-scale data processing.
3. Asynchronous Processing (asyncio module)
   Best suited for I/O-bound tasks such as network communication and file operations. Asynchronous processing improves efficiency by handling waiting times more effectively.

3. Multiprocessing vs. Multithreading

Impact of GIL (Global Interpreter Lock)

Python has a mechanism called the Global Interpreter Lock (GIL), which ensures that only one thread runs at a time. This limitation prevents performance improvements in CPU-bound tasks, even if multiple threads are used. As a result, multithreading is only effective for I/O-bound tasks that involve a lot of waiting time.

Advantages and Limitations of Multithreading

Threads are lightweight and ideal for I/O-bound tasks such as file operations and network processing. However, due to the GIL, multithreading cannot fully utilize multiple CPU cores, making it unsuitable for CPU-intensive tasks.

“`
import threading
import time

def worker(num):
print(f”Worker {num} starting”)
time.sleep(2)
print(f”Worker {num} finished”)

threads = []for i in range(5):
t = threading.Thread(target=worker, args=(i,))
threads.append(t)
t.start()

for t in threads:
t.join()
“`

This code executes five threads simultaneously, each sleeping for two seconds before finishing. Using multithreading allows tasks to proceed concurrently.

Advantages of Multiprocessing

To bypass GIL limitations, multiprocessing is an effective alternative. Unlike threads, processes operate independently in separate memory spaces, allowing them to fully utilize multiple CPU cores. This approach is particularly beneficial for computationally intensive tasks and large-scale data processing.

“`
from multiprocessing import Process
import time

def worker(num):
print(f”Worker {num} starting”)
time.sleep(2)
print(f”Worker {num} finished”)

if name == ‘main‘:
processes = []for i in range(5):
p = Process(target=worker, args=(i,))
processes.append(p)
p.start()

for p in processes:
    p.join()

“`

In this example, five processes run in parallel, each executing tasks independently. The join() method ensures that the program waits until all processes are completed before proceeding.

4. Implementing Parallel Processing in Python

Parallel Processing with the multiprocessing Module

The multiprocessing module enables efficient management of multiple processes. Below is a basic example that utilizes a process pool for parallel execution.

“`
from multiprocessing import Pool

def square(x):
return x * x

if name == ‘main‘:
with Pool(4) as p:
result = p.map(square, [1, 2, 3, 4, 5])
print(result)
“`

In this code, four processes execute simultaneously, each computing the square of elements in the list. The results are returned as a list, demonstrating the efficiency of parallel processing.

5. Asynchronous Processing and Its Applications

Asynchronous Processing with the asyncio Module

The asyncio module is particularly useful for tasks that involve waiting, such as network communication or file I/O. By handling waiting times efficiently, it allows other tasks to proceed concurrently.

“`
import asyncio

async def worker(num):
print(f’Worker {num} starting’)
await asyncio.sleep(1)
print(f’Worker {num} finished’)

async def main():
tasks = [worker(i) for i in range(5)]await asyncio.gather(*tasks)

asyncio.run(main())
“`

This code runs five tasks concurrently. Using await allows tasks to be executed asynchronously, meaning that other tasks can proceed during waiting periods.

6. Performance Tuning for Parallel Processing

Parallelization with Joblib

The Joblib library is designed to optimize heavy computations, such as data processing and machine learning model training. Below is an example of parallel processing using Joblib.

“`
from joblib import Parallel, delayed

def heavy_task(n):
return n ** 2

results = Parallel(n_jobs=4)(delayed(heavy_task)(i) for i in range(10))
print(results)
“`

By specifying n_jobs, the number of concurrent processes can be controlled. In this example, four processes execute calculations in parallel, returning the results as a list.

7. Practical Applications of Parallel Processing in Python

Data Processing and Web Scraping

Parallel processing in Python is particularly useful for handling large amounts of data, such as in data processing and web scraping. For instance, when crawling web pages, using multithreading or asynchronous processing enables multiple requests to be sent simultaneously, significantly reducing processing time. Additionally, in machine learning model training and data preprocessing, using multiprocessing or Joblib can enhance performance.

8. Conclusion

Parallel processing is an essential technique for maximizing Python’s performance. By appropriately utilizing modules such as threading, multiprocessing, asyncio, and Joblib, tasks can be efficiently handled across various scenarios. Consider implementing these techniques in real-world projects to optimize processing efficiency.