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Concurrency and parallelism are techniques used in Python (and other programming languages) to achieve efficient execution of multiple tasks or processes. While they are related concepts, they have distinct meanings:

Concurrency: Concurrency refers to the ability to execute multiple tasks or processes concurrently. It allows different parts of a program to make progress independently, even if they are not executed simultaneously. In Python, concurrency is commonly achieved through techniques such as threading, multiprocessing, or asynchronous programming.

Threading: Python’s threading module enables concurrent execution of multiple threads within a single program. Threads are lightweight and share the same memory space, allowing for faster context switching between tasks. However, due to the Global Interpreter Lock (GIL), which restricts simultaneous execution of Python bytecode, threading in Python may not achieve true parallelism for CPU-bound tasks.

Asynchronous Programming: Python’s asyncio module provides a higher-level approach to concurrency through asynchronous programming. It allows tasks to run concurrently, where one task can yield control to another task during I/O-bound operations, without blocking the entire program. Asynchronous programming is particularly useful for I/O-bound tasks, such as network communication or file operations, where waiting for data does not consume CPU resources.

Parallelism: Parallelism involves the simultaneous execution of multiple tasks or processes to achieve increased computational speed. In Python, parallelism can be achieved through techniques like multiprocessing or distributed computing frameworks.

Multiprocessing: The multiprocessing module in Python enables the execution of multiple processes in parallel, taking advantage of multiple CPU cores. Each process runs in a separate memory space, allowing true parallel execution and bypassing the limitations imposed by the Global Interpreter Lock (GIL). Multiprocessing is suitable for CPU-bound tasks that can be divided into independent subtasks.

Distributed Computing: Python also supports parallelism through distributed computing frameworks like Apache Spark or Dask. These frameworks enable the distribution of computations across multiple nodes or machines, allowing for high-performance parallel processing of large datasets.

It’s important to note that choosing between concurrency and parallelism depends on the nature of the tasks and the resources available. Concurrency is beneficial for I/O-bound operations, while parallelism is more suitable for CPU-bound tasks. Python provides various modules and frameworks to implement both concurrency and parallelism, allowing developers to optimize performance based on specific requirements.

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