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August 27, 2022 10:39 am GMT

A Python Multiprocessing and Parallel Programming Reference in Python

Multiprocessing and Parallel Programming Reference in Python

Speeding up calculations is a goal that everyone strives for. What if you had a script that could run 10 times faster than it does now? In this article, we will look at the Python multiprocessing and multiprocessing library.

An Overview of Parallelism

Before we go into Python programming, we should discuss parallel computing, which is a key idea in computer science.

  • When you run a Python script, your code eventually becomes a process running on one core of your CPU.
  • But since current computers have so many cores, what if you use more cores for your calculations? As a result, your calculations will be faster.
  • Let us assume this as a general premise for the time being, but as we will show later in this essay, it is not universally true.
  • Without going into too much detail, the idea behind parallelism is to construct your code so that it can utilize many CPU cores.

Computing in Parallel and Serial

Consider yourself alone with a massive problem to solve. You need to find the square root of eight different numbers.

  • You don't have many choices as to what you're going to do.
  • You start with the first number and work your way to the end. Then you continue with the others.
    What if you have three math-savvy friends who are eager to help you out?

  • Each of them will calculate the square root of two numbers, making your job easier because the workload will be divided equally among your friends.

  • This indicates that your problem will be solved soon.

Models for Parallel Programming

We know what parallel programming is, but how do we use it?
As stated earlier, parallel computing involves the execution of multiple tasks on multiple cores of the CPU, implying that those operations are conducted simultaneously.
Before you start thinking about parallelization, there are a few things you should think about.

  • Exactly parallel. The tasks can be completed independently and do not need to communicate with one another.
  • Parallelism of shared memory
  • Processes must communicate with one another, therefore they share a global address space.
  • Message exchange. When messages are needed, processes must share them.

Python Multiprocessing - Process-based Parallelism in Python

The multiprocessing module is a technique for implementing parallelism in Python.
Using the multiprocessing module, you can create multiple processes, each with its own Python interpreter.

  • As a result, Python multiprocessing achieves process-based parallelism.
  • You may have heard of other libraries, such as threading, which is also included with Python, but there are significant differences between them.
  • The multiprocessing module generates new processes, whereas the threading module generates new threads.

Benefits of Using Multiprocessing

  • Improved CPU utilization when dealing with CPU-intensive tasks
  • Greater influence over a child than threads
  • Simple to program

The first benefit is related to performance. Since multiprocessing creates additional processes, you can significantly leverage your CPU's computational power by distributing your duties across other cores.

  • Nowadays, most processors are multi-core, and by optimizing your code, you can save time by doing calculations in parallel.
  • A second benefit considers multithreading as an alternative to multiprocessing.
  • On the other hand, texts are not processes that produce effects.
  • When you start a thread, destroying or interrupting it is also dangerous.
  • As a comparison of multiprocessing and multithreading is beyond the scope of this article, I suggest you do more research on the subject.

How to Begin with Python Multiprocessing

We'll start with a very simple example and use it to demonstrate the fundamentals of Python multiprocessing.

  • The parent process. There is only one parent process, which can have several children.
  • The child process. The parent creates this. Each child can have new children.

We'll employ the child process to do a certain purpose. In this manner, the parent can continue with its execution.

Getting the Most Out of Python Multiprocessing

  • Multiple processes and parallel computations are not always more efficient than serial computations.
  • Serial processing is faster than the parallel calculation for low CPU-intensive workloads.
  • As a result, it's critical to understand when to use multiprocessing, which is dependent on the tasks at hand.

Conclusion

In this article, we discussed how to use Python multiprocessing to improve the performance of Python code.

  • First, we discussed what parallel computing is and the various approaches for employing it. Then we talked about multiprocessing and its benefits.
  • Finally, we discovered that parallelizing computations is not always the best option and that the multiprocessing module should be utilized to parallelize CPU-bound operations.
  • As always, analyze the unique problem at hand and weigh the benefits and drawbacks of the various solutions.

Original Link: https://dev.to/chennaiseo2/a-python-multiprocessing-and-parallel-programming-reference-in-python-1dl

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