Differences between Python Parallel Threads and Processes

In Python, parallelism is a powerful concept used to execute multiple tasks concurrently by improving performance and efficiency. The Two common approaches to parallelism in Python are parallel threads and parallel processes. While both achieve concurrent execution they have distinct characteristics and are suitable for the different use cases.

Differences Between Python Parallel Threads and Processes

Below, are the differences between Python Parallel Threads and Processes in Python

What is Parallel Threads?

The Parallel threads involve executing multiple threads within the same process and sharing the same memory space. The Threads are lightweight and context switching between the threads is faster compared to the processes. An example of the parallel threads in Python is the use of a threading module.

Example: In this example, the below code employs threading in Python to execute the function GFG() concurrently across multiple threads, creating three threads and synchronizing their completion using the join() method. This enables parallel execution of the function and demonstrates the power of concurrency in Python.

import threading
def GFG():
    for i in range(5):
        print(i)
# Create multiple threads
threads = []
for _ in range(3):
    thread = threading.Thread(target=GFG)
    threads.append(thread)
    thread.start()
# Wait for all threads to complete
for thread in threads:
    thread.join()

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What is Parallel Processes?

The Parallel processes involve executing the multiple processes and each with its memory space. The Processes are heavier than threads but offer better isolation and are suitable for the CPU-bound tasks. An example of the parallel processes in Python is the multiprocessing module.

Example : In this example, below code uses the multiprocessing module in Python to create multiple processes that concurrently execute the function GFG(), printing numbers from 0 to 4. It demonstrates parallel execution by starting three processes and synchronizing their completion using the join() method.

from multiprocessing import Process
def GFG():
    for i in range(5):
        print(i)
# Create multiple processes
processes = []
for _ in range(3):
    process = Process(target=GFG)
    processes.append(process)
    process.start()
# Wait for all processes to complete
for process in processes:
    process.join()

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Differences Between Python Parallel Threads and Processes

Below, are the Differences between Python Parallel Threads and Processes in table format.

Conclusion

In conclusion, parallel threads and processes offer concurrent execution but differ in the their memory usage, isolation, context switching and suitability for the different types of the tasks. Threads are lightweight and suitable for the I/O-bound tasks in while processes provide the better isolation and are suitable for the CPU-bound tasks.