Difference Between Multiprocessing and Threading in Python

Difference Between Multiprocessing and Threading in Python

Vaibhav Vaibhav Jan-22, 2022 Nov-03, 2021 Python Python Threading
  1. Python Multiprocessing
  2. Python Threading
  3. Multiprocessing VS Threading in Python

Multiprocessing and Threading are two of the most important concepts that every computer science enthusiast should know. These two are essential techniques that have the same aim to run multiple programs or some piece of code concurrently. Although their end goal is the same, they are very different from each other. In this article, we will learn about both of them and how they are different from each other.

Python Multiprocessing

Multiprocessing is the technique of utilizing more than one CPU or processor at a time to run multiple processes. This technique is widely used to fast-forward some computational tasks by utilizing the availability of multiple processors. Note that if the computer does not have extra processors, multiprocessing will not work no matter what code we write because it is a prerequisite.

In real-world applications, multiprocessing is widely used to get things done faster. This technique promotes the use of the full potential of a computer system and cuts down costs if someone is using a rented computer system or a cloud-based service.

Multiprocessing utilizes processes. A process is an isolated and independent program that is under execution. A process has its own separate memory space, code, data, files, and stack. Generally, processes are heavyweight and require more resources, as compared to threads. With processes, inter-process communication is slow because processes have different memory addresses and spaces.

Python Threading

As the name suggests, threading uses threads. Threads are segments of processes that share memory space, code, data, and files with the parent process or the process that created them. As compared to processes, threads are lightweight, easier to create, and require fewer resources. Inter-process communication is fast with threads because they share memory space with their parent process.

Threading is the technique of running multiple threads concurrently. Like multiprocessing, threading also aims to execute operations faster by utilizing the full potential of computer systems. Since threads belong to a single process, all the operations are performed inside a single process, unlike multiprocessing, where multiple processes are created to get the job done.

Although multithreading is fast and robust, it requires careful attention because programs can run into deadlocks and race conditions. Hence, there should be a structured way of using threads and associated resources.

Multiprocessing VS Threading in Python

Multiprocessing Threading
Uses processes. Uses threads.
Heavyweight and large memory footprint. Lightweight and low memory footprint.
Processes have their own independent memory space. Threads have a shared memory space with the parent process.
Inter-process communication is a bit difficult due to different memory addresses and spaces. Inter-process communication is easy due to shared memory spaces.
Creating new processes is slower. Creating new threads is faster.
In Python, multiprocessing can be used to implement true parallelism. It utilizes multiple CPUs and processors behind the scenes. In Python, threading does not work due to GIL or Global Interpreter Lock. GIL is a part of the default Python interpreter cPython.
In Python, multiprocessing can practically be implemented using the multiprocessing module. In Python, multiprocessing can be practically implemented using the threading module.
Requires more system resources to execute. Requires fewer system resources to run.
Vaibhav Vaibhav avatar Vaibhav Vaibhav avatar

Vaibhav is an artificial intelligence and cloud computing stan. He likes to build end-to-end full-stack web and mobile applications. Besides computer science and technology, he loves playing cricket and badminton, going on bike rides, and doodling.

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