Multiprocessing vs Multithreading in Python Part 1


With separate memory spaces, an overflow must happen in order to damage nearby processes, which is a more unlikely thing to happen than the situation exposed by marcelo. Threading’s job is to enable applications to be responsive. Suppose you have a database connection and you need to respond to user input. Without threading, if the database connection is busy the application will not be able to respond to the user. By splitting off the database connection into a separate thread you can make the application more responsive. Also because both threads are in the same process, they can access the same data structures – good performance, plus a flexible software design.

For me, this actually ran the first function and then the second function but this is due to that launching a process is expensive and the first one finished when starting the second one. If these were longer running functions we would see that the functions were executed parallel and really at the same time if we had free cores. Of course, this is not good for executions that require greater computation, as the GIL/lock upon the thread would remain. In this case, multiprocessing is beneficial, allowing you to split your workload across multiple CPU cores. CPU-bound refers to a condition when the time for it to complete the task is determined principally by the speed of the central processor.

Data transfer, hardware cache-levels and other issues will almost certainly reduce this sort of performance gain in “real” codes. Notice that the user and sys both approximately sum to the real time. This is indicative that we gained no benefit from using the Threading library.

Understanding Multiprocessing and Multithreading in Python –

Understanding Multiprocessing and Multithreading in Python.

Posted: Mon, 22 Aug 2022 07:00:00 GMT [source]

Both multithreading and multiprocessing allow Python code to run concurrently. Only multiprocessing will allow your code to be truly parallel. However, if your code is IO-heavy , then multithreading will still probably speed up your code. In the original article, I mentioned that the Python multiprocessing module would be easier to drop into existing code than the threading module. This was because the Python 3 threading module required subclassing the Thread class and also creating a Queue for the threads to monitor for work. The Python multiprocessing module is easier to drop in than the threading module, as we don’t need to add a class like the Python multithreading example.

For a I/O-bound task in Python, multi-thread could be used to improve the program performance. Real Python has already given a good tutorial with code examples on Python concurrency. I have also borrowed some good diagrams from their tutorial and the readers should give the credits to them on those illustrations.

Introduction to Concurrency and Multithreading

Here the CPU is just doing nothing but is waiting for an answer. Multithreading is a technique where multiple threads are spawned by a process to do different tasks, at about the same time, just one after the other. This gives you the illusion that the threads are running in parallel, but they are actually run in a concurrent manner.

If we had then we would expect the real time to be significantly less. These concepts within concurrent programming are usually known as CPU-time and wall-clock time respectively. Threading in simple words, what it does is creates threads and get them executed independently, and returns its result to the main process. Threads are small subprocess that can get executed easily and independently. Without multiprocessing, Python programs have trouble maxing out your system’s specs because of the GIL .

Each process has its own memory space it uses to store the instructions being run, as well as any data it needs to store and access to execute. The multiprocessing module provides easy-to-use process-based concurrency. Processes can be used for IO-bound tasks in the same way that threads can be, although there are major limitations to using processes. This means that although we may have multiple threads in our program, only one thread can execute at a time. This means that functions executed in new threads can access the same data and state. These might be global variables or data shared via function arguments.

As we can see, it clearly rotated between the lines it executed from each of these functions. We first define what it means to run code synchronously, asynchronously , concurrently and parallel , at least from Python’s perspective. Then we go through when to use which and the challenges related to each of the multitasking options. For example, given a list of numbers, computing the sum of all the numbers in the list.

Threading Library

Since the processes Process-1 and Process-2 are performing the task of asking their own CPU core to sit idle for a few seconds, we don’t find high Power Usage. But the creation of processes itself is a CPU heavy task and requires more time than the creation of threads. Hence, it is always better to have multiprocessing as the second option for IO-bound tasks, with multithreading being the first.


Parallel processing can be achieved either by running code simultaneously on different core or on the same core by utilizing CPU cycles. Multiprocessing on the other hand creates multiple processes, not threads. The Parent Process issues child processes and the child processes do their work and return the result to the parent process. The Work of a Port_Scanner basically, is to talk to the specified IP address on the specified port and say whether it is open or not. Though it seems simple, it takes a lot of time to scan all ports of an IP. This is where threading and multi_processing come into play.

Multiprocessing Vs Threading in Python

Multi-threaded applications are applications that have two or more threads that run concurrently. For high-performance workloads, the program should process as much data as possible. Second, note that multi-threading and multiprocessing may not be useful here . If the operations are most CPU-bound , multi-threading maybe won’t speed up stuff. If the operations are already optimized to use multiple threads, multiprocessing maybe won’t speed it up. 2) Multithreading is best used for IO (Input/Output) based tasks like querying a database or loading a webpage.

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In order to use multiple processing the multiprocessing module is used. As you can see this module is consumed is much the same as way the threading module`. When you run this you will see that only a single Python process is run, even though multiple threads are executed. Using Python asyncio, we are also able to make better use of the CPU sitting idle when waiting for the I/O.

Here’s an example to better understand Python multiprocessing. The lock allows the developer to ensure that one function can perform the following operations. You will want threads to be able to modify the variables that are common between threads.

Python – Threading vs Multiprocessing

There it is—just swap threading.Thread with multiprocessing.Process and you have the exact same program implemented using multiprocessing. As you can see, the API for spinning up a new thread to a task in the background is pretty straightforward. What’s great is that the API for multiprocessing is almost the exact same as well; let’s check it out. As you all know, data science is the science of dealing with large amounts of data and extracting useful insights from them. You now know the difference between threading and multiprocessing and when to use each.

GPUs are the state-of-the-art hardware in this regard, being designed to python multiprocessing vs threading large chunks of data in parallel. True parallelism can ONLY be achieved using multiprocessing. That is because only one thread can be executed at a given time inside a process time-space. Multithreading is a program execution technique that allows a single process to have multiple code segments . It also runs concurrently within the “context” of that process.

This reduced the total execution time of our whole program by a significant 50%. Hence, multithreading is the go-to solution for executing tasks wherein the idle time of our CPU can be utilized to perform other tasks. Since threads share the same memory location within a parent process, special precautions must be taken so that two threads don’t write to the same memory location. CPython interpreters use the Global Interpreter Lock mechanism to prevent multiple threads from executing Python bytecodes at the same time. This lock is very important as CPython’s memory management is not thread-safe.

multiprocessing in python

First of all, let’s look at the differences between a thread and a process at a general level. Suppose we have this task which we will execute many times. I’m going to start with a simple experiment and I will borrow the code from this article written by Brendan Fortuner which is a great read by the way. All experiments are conducted on a machine with 4 cores (EC2 c5.xlarge). In this article, I will try to discuss some misconceptions about Multithreading and explain why they are false. Difference between @classmethod, @staticmethod, and instance methods in Python.

However, If the code is CPU-bound and your machine has multiple cores, multiprocessing would be a better choice. With threading, concurrency is achieved using multiple threads, but due to the GIL only one thread can be running at a time. In multiprocessing, the original process is forked process into multiple child processes bypassing the GIL. Each child process will have a copy of the entire program’s memory.

Scikit-Learn Cheatsheet: Methods For Classification and Regression

It’s important to note, that when we saymultitasking,what we mean is that each task is making progress towards its completion. However, there’s no guarantee that at any moment, both of those tasks are being worked on at the same time. I think also multithread wont work for freedback loops unless you somehow cleverly split it, process and merge later. You’d have to write different code for different feedback loops, if it ever possible.

  • If your code is CPU bound, multiprocessing is most likely going to be the better choice—especially if the target machine has multiple cores or CPUs.
  • Difference between @classmethod, @staticmethod, and instance methods in Python.
  • GIL is a global lock which locks everything out and allows only single thread execution utilizing only a single core.

Threads have a lower overhead compared to processes; spawning processes take more time than threads. You can see that I’ve created a function func that creates a list of random numbers and then multiplies all the elements of it sequentially. This can be a fairly heavy process if the number of items is large enough, say 50k or 100k. When performing IO-operations, we very likely will need to move data between worker processes back to the main process. This may be costly if there is a lot of data as the data must be pickled at one end and unpickled at the other end.

multiprocessing and multithreading

If you use, each process gets its own GIL and uses more memory, so it’s a trade off. Note also that due to GIL , only multiprocessing is truly parallelized. There can only be one thread running at any given time in a python process.

Although this data serialization is performed automatically under the covers, it adds a computational expense to the task. Therefore, the tasks we execute with a threading.Thread should be tasks that involve IO operations. Interacting with devices, reading and writing files, and socket connections involve calling instructions in your operating system , which will wait for the operation to complete. The operations involve input and output , and the speed of these operations is bound by the device, hard drive, or network connection. Thread-based concurrency supports parallelism, whereas process-based concurrency supports full parallelism. Typically sharing data between processes requires explicit mechanisms, such as the use of a multiprocessing.Pipe or a multiprocessing.Queue.