What is multiprocessing, and how does it differ from multithreading?

Multiprocessing in Python: Multiprocessing is a programming paradigm that involves the simultaneous execution of multiple processes. In Python, the multiprocessing module provides a convenient way to create and manage parallel processes. Here are key points differentiating multiprocessing:

• Independence of Processes:

  • In multiprocessing, each process has its own memory space, ensuring independence.
  • This isolation prevents one process from affecting the data of another, enhancing stability.

• Parallel Execution:

  • Multiprocessing allows for true parallelism, as processes run independently of each other.
  • Ideal for CPU-bound tasks, where the program's speed can be significantly improved through parallel processing.

• Resource Utilization:

  • Utilizes multiple CPUs or cores efficiently, distributing the workload across them.
  • Well-suited for computationally intensive tasks and data processing.

Multithreading in Python: Multithreading, on the other hand, involves the concurrent execution of multiple threads within the same process. The threading module in Python facilitates multithreading. Let's explore the characteristics that differentiate multithreading:

• Shared Memory:

  • Threads within a process share the same memory space, making communication between them simpler.
  • However, this shared space requires careful synchronization to avoid conflicts.

• Concurrency, not Parallelism:

  • Multithreading is suitable for I/O-bound tasks, where threads can overlap during waiting times.
  • Threads are executed concurrently, but due to Global Interpreter Lock (GIL) in CPython, they don't achieve true parallelism.

• Lightweight:

  • Threads are lighter compared to processes, resulting in lower overhead.
  • Efficient for tasks involving I/O operations, like file handling or network communication.

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