multithreading Multiprocessing vs Threading Python

multiprocessing process
concurrency primitives

19.4 seconds isn’t terribly long, but what if we wanted to download more pictures? With an average of 0.2 seconds per picture, 900 images would take approximately 3 minutes. The good news is that by introducing concurrency or parallelism, we can speed this up dramatically. In PRACTISE on different platforms spawning multiple threads ends up like spawning one process. They don’t have the same use cases at all though, so I’m not sure I agree with that.


This prevents the text from getting jumbled up when you print. You’ll often want your threads to be able to use or modify variables common between threads. Threading is game-changing, because many scripts related to network/data I/O spend the majority of their time waiting for data from a remote source.

When it comes to IO, Multiprocessing is overall as good as multithreading. It just has more overhead because popping processes is more expensive than popping threads. Actually for cpu heavy tasks, multithreading is useless indeed. Each time one job will be executed a little and then the other takes on. Here is a detailed comparison between Python multithreading and multiprocessing.

Difference between Multithreading and Multiprocessing

In this article, we will discuss the tips and tricks that reduce time and increase performance in machines. We will walk you through Python multiprocessing vs multithreading, and how and where to implement these approaches. The above example shows that python threading seems to outperform python multiprocessing as it is approximately 9 times faster than the other.

Python wasn’t designed considering that personal computers might have more than one core . Multithreading is critical for production applications because it unlocks features and capabilities that are otherwise out of reach. Most programming languages have support for multithreading, but it can be hard to decide which language is the best for writing your application. So processes and threads are both different operating system constructs.

The following code will outline the interface for the Threading library but it will not grant us any additional speedup beyond that obtainable in a single-threaded implementation. When we come to use the Multiprocessing library below, we will see that it will significantly decrease the overall runtime. Saving is IO bound, so there is no need to use multiple processes. In this article, we discuss what it means in practice for a task to run async vs in a separate thread vs in a separate process. I’ll also try my best to illustrate how these work, the differences and pitfalls so you hopefully avoid the same mistakes I did with these when I implemented them into my projects. In this article we will look at threading vs multiprocessing within Python, and when you should use one over the other.

  • Multiprocessing are classified according to the way their memory is organized.
  • On memory differences these are in a capEx up-front cost sense.
  • In Multiprocessing, the creation of a process, is slow and resource-specific whereas, in Multiprogramming, the creation of a thread is economical in time and resource.
  • An IO-bound task is a type of task that involves reading from or writing to a device, file, or socket connection.

Each program is a process and has at least one thread that executes instructions for that process. A thread refers to a thread of execution in a computer program. One of the most requested items in the comments on the original article was for an example using Python 3’s asyncio module.

What is Threading in Python

For now I have kept it very short because I aim to write short articles to absorb the information well. I will post more articles on this topic in the near future. We saw six different approaches to perform a task, that roughly took about 10 seconds, based on whether the task is light or heavy on the CPU. A program is an executable file which consists of a set of instructions to perform some task and is usually stored on the disk of your computer.

Multiprocessing fragments multiple processes into routines that run independently. This ensures that every processor gets its own core for smooth execution. For users who prefer Object-Oriented Programming, multithreading can be implemented as a Python class that inherits from threading.Thread superclass.

Note that the output might be printed in unordered fashion as the processes are independent of each other. Each process executes the function in its own default thread, MainThread. Open your Task Manager during the execution of your program. You can see 3 instances of the Python interpreter, one each for MainProcess, Process-1, and Process-2.

Multithreading vs Multiprocessing – Difference Between Them

I always thought that threads execute code simultaneously, but this is totally untrue in Pyhton. This is a mutex that allows only one thread to hold control of the Python interpreter. This means that the GIL allows only one thread to execute at a time even in a multi-threaded architecture.

Because all the processes are independent to each other, and they don’t share memory. To do collaborative tasks in Python using multiprocessing, it requires to use the API provided the operating system. Now that we got a fair idea about multiprocessing helping us achieve parallelism, we shall try to use this technique for running our IO-bound tasks. We do observe that the results are extraordinary, just as in the case of multithreading.

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Multiple threads live in the same process in the same space, each thread will do a specific task, have its own code, own stack memory, instruction pointer, and share heap memory. @ArtOfWarfare Threads can do much more than raise an exception. A rogue thread can, via buggy native or ctypes code, trash memory structures anywhere in the process, including the python runtime itself, thus corrupting the entire process. And, threads share I/O scheduling — which can be a bottleneck.

Example Multithreading with Python:

Process method is similar to the multithreading method above where each Process is tagged to a function with its arguments. In the code snippet below, we can see that the time taken is longer for multiprocessing than multithreading since there is more overhead in running multiple processors. Now let’s introduce some parallelizability into this task to speed things up. Before we dive into writing the code, we have to decide between threading and multiprocessing.

  • The GIL is used within each Python process, but not across processes.
  • If you have a large amount of data and you have to do lot of expensive computation, then with multiprocessing you can utilize multiple CPUs parallely to speed up your execution.
  • Each thread creates a new list and adds random numbers to it.
  • I/O bound refers to a condition when the time it takes to complete a computation is determined principally by the period spent waiting for input/output operations to be completed.
  • Note that, it is the same MainProcess that calls our function twice, one after the other, using its default thread, MainThread.
  • Now in case of self-contained instances of execution, you can instead opt for pool.

The same can be done with multiprocessing—multiple processes—too. In fact, most modern browsers like Chrome and Firefox use multiprocessing, not multithreading, to handle multiple tabs. To use multiple processes, we create a multiprocessing Pool. With the map method it provides, we will pass the list of URLs to the pool, which in turn will spawn eight new processes and use each one to download the images in parallel.

We also need at least one worker to listen on that job queue. To demonstrate concurrency in Python, we will write a small script to download the top popular images from Imgur. We will start with a version that downloads images sequentially, or one at a time. As a prerequisite, you will have to register an application on Imgur. If you do not have an Imgur account already, please create one first. Threading is just one of the many ways concurrent programs can be built.

This is because of a technical reason behind the way the Python interpreter was implemented. CPUs are very fast, and we often have more than one CPU core on a single chip in modern computer systems. We would like to perform our tasks and make full use of multiple CPU cores in modern hardware. The GIL is used within each Python process, but not across processes.

Much of the rest of the “multiprocessing” module provides tools to work with multiprocessing.Process instance, that mimics the threading module. The “multiprocessing” module provides process-based concurrency in Python. Multiprocessing is for times when you really do want more than one thing to be done at any given time. Suppose your application needs to connect to 6 databases and perform a complex matrix transformation on each dataset.

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In the python multiprocessing vs threading process, each thread runs parallel to each other. However, because each CPU are separate, it may happen that one CPU may not have anything to process. One processor may sit idle, and the other may be overloaded with the specific processes. In such a case, the process and resources are shared dynamically among the processors. In Multiprocessing, the creation of a process, is slow and resource-specific whereas, in Multiprogramming, the creation of a thread is economical in time and resource. In order to actually make use of the extra cores present in nearly all modern consumer processors we can instead use the Multiprocessing library.

To do this, you will need to use a lock that locks the variable it wants to modify. When another function wants to use a variable, it waits until that variable gets unlocked. In general processes, a thread can be interrupted by the computer according to the situation. But in the case of Python 3, the threads appear to be executing simultaneously.

Other answers have focused more on the multithreading vs multiprocessing aspect, but in python Global Interpreter Lock has to be taken into account. When more number of threads are created, generally they will not increase the performance by k times, as it will still be running as a single threaded application. GIL is a global lock which locks everything out and allows only single thread execution utilizing only a single core. The performance does increase in places where C extensions like numpy, Network, I/O are being used, where a lot of background work is done and GIL is released. Using Python multiprocessing, we are able to run a Python using multiple processes. In principle, a multi-process Python program could fully utilize all the CPU cores and native threads available, by creating multiple Python interpreters on many native threads.

separate memory

We are now going to utilise the above two separate libraries to attempt a parallel optimisation of a “toy” problem. Whenever a function wants to modify a variable, it locks that variable. When another function wants to use a variable, it must wait until that variable is unlocked. Like another commenter said though, there’s clearly a problem to be solved with the need of this program versus the execution.

multithreading vs multiprocessing

On differences these are in a capEx up-front cost sense. Indeed, multithreading is usually implemented to handle GUIs. Threads are faster to start than processes and also faster in task-switching. Multiprocessing system takes less time whereas for job processing a moderate amount of time is taken.

Due to this, the Python multithreading module doesn’t quite behave the way you would expect it to if you’re not a Python developer and you are coming from other languages such as C++ or Java. If you haven’t read it yet, I suggest you take a look at Eqbal Quran’s article on concurrency and parallelism in Ruby here on the Toptal Engineering Blog. Multiprocessing and Multithreading both adds performance to the system. A crashing process won’t bring down other processes, whereas a crashing thread will probably wreak havoc with other threads. A great use case for this is long-running back-end tasks for web applications. If you have some long-running tasks, you don’t want to spin up a bunch of sub-processes or threads on the same machine that need to be running the rest of your application code.

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