Concurrency vs Parallelism

Concurrency and parallelism are two terms that are often used in the context of multithreaded and multiprocess programming.

Here’s a brief explanation of each:

🔗Concurrency:

Concurrency is when two or more tasks can start, run, and complete in overlapping time periods. It doesn’t necessarily mean they’ll ever both be running at the same instant (e.g., multiple threads on a single-core machine). In Python, you can achieve concurrency through threading, asyncio, and other libraries. Concurrency is about dealing with a lot of things at once.

🔗Parallelism:

Parallelism is when tasks literally run at the same time, e.g., on a multicore processor. Parallelism is about doing a lot of things at once.

In the context of Python, due to the Global Interpreter Lock (GIL), Python threads are not truly parallel and are suitable for I/O-bound tasks. For CPU-bound tasks, you would use multiprocessing to achieve true parallelism, as each process gets its own interpreter and memory space, thus bypassing the GIL.

🔗Why does it matter?

Efficiency: Concurrency and parallelism can make your program more efficient. They allow you to structure your program so it can do more than one thing at a time, which is especially important for tasks involving I/O or computation.
Performance: For CPU-bound tasks, parallelism allows you to take full advantage of multiple cores and get the job done faster. For I/O-bound tasks, concurrency allows your program to continue doing work while waiting for I/O operations to complete.
Responsiveness: In the context of web servers and GUI applications, concurrency and parallelism can make your application more responsive. They allow your application to continue handling new requests or user interactions while other tasks are running.

Remember, while concurrency and parallelism can make your program faster and more responsive, they also add complexity to your program and can make it harder to reason about. It’s important to understand these concepts well and use them appropriately. 😊