Python is a powerful programming language with several advantages that make it stand out. It’s high-level, object-oriented, and uses a human-readable, dynamically typed syntax, just to name a few benefits.
While there are plenty of advantages that come with using Python, we will be focused on only one of them in this post. The Python language allows for something called multiprocess, a term that describes the act of running many processes simultaneously. With it, you can write a program and assign many tasks to be completed at the same time, saving time and energy.
Without further ado, let's slither right into this post and discover what this means for you.
What is multiprocess?
Multiprocess is a Python package that supports spawning processing tasks using an API similar to the Python threading module. In addition, the multiprocessing package supports concurrency in both local and remote types, allowing you to bypass the global interpreter lock that comes with threading.
Now that we have discussed this concept, let's review its benefits.
How does multiprocess work?
Multiprocess is assigning tasks to multiple processes — workers — to handle tasks alongside other tasks. This is a significant benefit to programmers as it allows you to create more efficient software that can perform tasks and functions. In addition, with multiprocess, we can save time as the software will be able to run multiple tasks without slowing down at all.
This is not the only benefit of multiprocess; improved software performance also enhances the hardware performance for any device the software runs on. In addition, by using multiprocessing in Python, you can tap into the maximum processing power of the devices the software will ultimately run on.
The following video shows how python multiprocessing works and how to get started with it.
The Benefits and Drawbacks of Multiprocess
The benefits and drawbacks of multiprocess are worth discussing since not all software will benefit from it. Therefore, it is essential to know when and where to use multiprocessing over other methods of concurrent task management.
The overhead that comes with multiprocessing is high, and it requires heavy use of the CPU. However, it runs much faster than threading does and has fewer limitations. Furthermore, the entire memory is copied into each subprocess which can be cumbersome to more extensive and complicated programs.
The code needed to import and use the multiprocess package in Python is relatively straightforward and can be accomplished in its most basic form with only a few simple lines of code. Let’s take a moment to look at what that code would look like.
if __name__ == '__main__':
for i in range(5):
p = multiprocessing.Process(target=spawn)
p.join() # this line allows you to wait for processes
The above code starts by importing the multiprocessing package, then by defining the spawn object. Next, the code then uses an if statement to check that the name of the spawned process is called __main__. Following that, it loops through the processes within the range of five, assigning each process to a spawned instance.
Once that is complete, the final two lines in this code start the process but only join them when the current process — if there are any — is finished. As a final note, it is essential to note that if you need to pass arguments to your new processes, you can do this by modifying the following line of code to include any potential data with the args parameter. Let’s look at that next.
p = multiprocessing.Process(target=spawn, args=(i))
Instead of only spawning the new process, we also pass in an additional parameter called args that will have the value of our current iteration item.
Using the Python Multiprocessing Package
Hopefully by now, you have learned a lot about the basics of Python multiprocessing as well as how it works. As with any programming language and its features, there is a lot more to it, and it will take some practice to nail down the nuances of using it. Regardless, you have everything you need to start your journey into the realm of using multiprocessing in your software development.