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Python Parallel Processing using MultiThreading !!!

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  An Introduction to Python Threading: Folks, you can go through the multiprocessing in Python in the below blog. Python Multiprocessing !!! A thread is an entity within a process that can be scheduled for execution. Also, it is the smallest unit of processing that can be performed in an OS (Operating System). In simple words, a thread is a sequence of such instructions within a program that can be executed independently of other code. For simplicity, you can assume that a thread is simply a subset of a process! If you want your application to make better use of the computational resources of multi-core machines, you are advised to use multiprocessing or concurrent.futures.ProcessPoolExecutor. However, threading is still an appropriate model if you want to run multiple I/O-bound tasks simultaneously.                                             ...
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Python Parallel Processing using MultiProcessing !!!

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 INTRODUCTION : In the world of Python programming, efficient execution of required tasks is essential for building high-performance applications. Asynchronous programming and multiprocessing are a few powerful techniques that can help you achieve parallelism in your code, improving its speed and responsiveness. However, understanding when and how to use threads and async can be decisive considering available space and CPU cores. Here, we will discuss and execute the multiprocessing in Python, which will help us to execute similar(repeating) tasks parallelly. Sequential Processing: Sequential processing involves executing tasks one after another, following a specific order. The next process starts only after the completion of the previous one, and the same flow goes on. Sequential processing utilizes less CPU and memory compared to parallel processing.  The main disadvantage of using this processing is that it takes more time as a single instruction is getting executed at a gi...
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