Tue. Dec 5th, 2023
Multiprocessing and Multithreading in Python

Python’s ability to use both multithreading and multiprocessing for parallel computing makes it a powerful choice for developers when choosing the best option for their application.

In this blog post, we will explore the why, when, and how of using Python multithreading and multiprocessing for your application.

We will explore the advantages and disadvantages of each approach, and discuss when it is most appropriate to choose one over the other.

By the end of this post, you will have a better understanding of when to choose Python multiprocessing for your application.

What is multiprocessing?

Multiprocessing is a technique used to enhance the performance of a program by dividing its workload among multiple processes.

A process is an instance of a program that is running on a computer, and each process has its own memory space, program counter, and stack.

In multiprocessing, the work is divided into smaller units and each unit is assigned to a separate process, allowing them to execute concurrently on different CPUs or cores.

In Pytho, the multiprocessing module provides a way to create and manage separate processes, each with its own resources, allowing for efficient use of system resources.

By using multiprocessing, you can leverage the full power of your system, by utilizing multiple CPUs and cores, and thereby increasing the speed and efficiency of your program.

In summary, multiprocessing allows a program to use multiple processors to run separate parts of a program concurrently, thereby improving performance and reducing the time taken to execute a program.

What are the benefits of using multiprocessing?

Multiprocessing offers several benefits for Python developers. First and foremost, it allows them to leverage multiple processors or cores available on a computer.

By doing so, multiprocessing enables the application to execute multiple tasks simultaneously, leading to significant performance improvements.

Moreover, multiprocessing enables the developer to perform resource-intensive tasks without blocking the main program’s execution.

For instance, an application that performs image processing or data analysis tasks can leverage multiprocessing to distribute the work across multiple cores, resulting in faster results.

Multiprocessing is also useful for applications that need to interact with external resources, such as databases or APIs.

With multiprocessing, the developer can parallelize the resource access, leading to improved response times and better resource utilization.

Finally, multiprocessing provides better fault tolerance than multithreading. In multithreading, if one thread crashes, it can cause the entire application to crash.

However, in multiprocessing, the processes run in separate memory spaces, and hence, if one process crashes, it doesn’t affect other processes.

In summary, the benefits of using multiprocessing include improved performance, better resource utilization, improved fault tolerance, and faster execution of resource-intensive tasks. However, multiprocessing has some drawbacks, as we will discuss in the next section.

What are the drawbacks of using multiprocessing?

While multiprocessing offers many advantages, it also comes with a few drawbacks that you need to consider before implementing it in your application. Here are some of the main drawbacks of using multiprocessing:

  1. Increased memory usage: Multiprocessing creates new processes that require additional memory, which can impact the overall performance of your application.
  2. More complex code: Using multiprocessing requires you to write more complex code compared to using single-threaded programs. This can make your code more difficult to read and maintain.
  3. Difficulty sharing data: Since each process has its own memory space, sharing data between processes can be more challenging. You may need to use additional techniques like inter-process communication (IPC) to share data between processes.
  4. Limited to CPU-bound tasks: Multiprocessing is best suited for CPU-bound tasks that require a lot of computation. For I/O-bound tasks like reading from a database or network, multiprocessing may not offer any performance improvements.
  5. Despite these drawbacks, multiprocessing can still be a great choice for certain types of applications. The key is to weigh the benefits against the drawbacks and determine whether multiprocessing is the best solution for your specific use case.


Multithreading is a way of executing multiple threads (sub-processes) of a single program simultaneously within a single process.

Each thread operates independently and shares the same memory space, allowing for faster program execution times and more efficient use of system resources.

One of the benefits of multithreading is that it can improve the performance of I/O bound programs (programs that spend a lot of time waiting for input/output operations to complete).

Multithreading allows a program to overlap I/O operations with computations, resulting in more efficient use of system resources and faster program execution times.

However, there are also drawbacks to using multithreading. One major drawback is that it can be difficult to debug and manage threaded programs, especially as the number of threads increases.

In addition, some programs may not benefit from multithreading, such as CPU-bound programs that spend most of their time performing computations rather than waiting for I/O operations.

To use multithreading in Python, you can utilize the threading module.

This module provides a Thread class, which can be used to create and manage multiple threads within a program.

When deciding between multiprocessing and multithreading, it is important to consider the nature of your program and its specific requirements.

If your program is I/O-bound, multithreading may be the best option. If your program is CPU-bound or requires heavy computation, multiprocessing may be a better choice.

Ultimately, it is important to weigh the benefits and drawbacks of each approach and determine which one is best suited for your specific use case.

How do I choose between multiprocessing and multithreading?

Now that we’ve covered what multiprocessing and multithreading are, as well as their benefits and drawbacks, it’s time to answer the question that’s likely on your mind: how do you decide between the two?

First and foremost, it’s important to consider what type of task you’ll be performing.

Generally speaking, if the task involves a lot of I/O operations, like reading and writing to a file, then multithreading may be the better choice. On the other hand, if the task is CPU-bound, meaning it involves a lot of computational work, then multiprocessing may be more effective.

Another consideration is the number of processors you have available. If you have a single processor, then multithreading may be more appropriate. However, if you have multiple processors available, then multiprocessing can help you make the most of your hardware.

It’s also important to keep in mind that both multiprocessing and multithreading come with their own set of complexities.

With multiprocessing, for example, you need to take care to avoid issues like race conditions and deadlocks.

Meanwhile, with multithreading, you need to consider issues like synchronization and context switching.

Ultimately, the decision between multiprocessing and multithreading will depend on your specific use case and the resources you have available.

By understanding the strengths and weaknesses of each approach, as well as the challenges involved in implementing them, you can make an informed decision that will help you get the most out of your Python code.

By Hari Haran

I'm Aspiring data scientist who want to know about more AI. I'm very keen in learning many sources in AI.

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