From: lexfridman
Understanding the nuances between parallelism, concurrency, and asynchronous programming in Python can significantly enhance your efficiency in writing high-performance code. This article will delve into these concepts, elucidating their distinctions and applications.
## Parallelism
Parallelism refers to the simultaneous execution of computations. This is typically achieved by leveraging multiple processors or cores, allowing different processes or threads to execute independently and at the same time. In Python, parallelism can be realized through multi-threading or multi-processing, though the presence of the [[challenges_and_benefits_of_the_global_interpreter_lock_in_python | Global Interpreter Lock in Python]] often makes multi-processing a more compelling choice, as it allows truly concurrent execution using separate memory space for each process.
> [!info] Parallelism in Practice
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> Parallel computing is primarily about using multiple copies of hardware, such as CPUs or cores, to perform computations simultaneously <a class="yt-timestamp" data-t="01:58:12">[01:58:12]</a>.
### Multi-threading
Multi-threading in Python can simulate parallelism through concurrency but is limited by the Global Interpreter Lock (GIL), which allows only one thread to execute Python bytecode at a time within a single process. This restriction often necessitates using multi-processing for parallel tasks that require full CPU utilization.
### Multi-processing
This technique involves utilizing multiple processes, where each has its own Python interpreter and memory space, thus bypassing the GIL limitation and enabling true parallel execution.
## Concurrency
Concurrency involves managing multiple computations that are in progress overlappingly. Unlike parallelism, which requires actual simultaneous execution, concurrency is more about dealing with lots of things at the same time. This is more of a software construct that allows a program to manage multiple tasks by interleaving their execution on a single processor by using tasks such as coroutines.
> [!info] Understanding Concurrency
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> Concurrency might give the illusion of parallelism but is achieved through interleaving multiple tasks on a CPU <a class="yt-timestamp" data-t="01:58:49">[01:58:49]</a>.
### Threads
Threads can be thought of as 'lightweight' versions of processes that live in the same process space and can access shared memory more easily than processes.
## Asynchronous Programming
Asynchronous programming in Python allows functions to run independently of the main program flow, thus avoiding blocking the execution. It is particularly effective in scenarios where tasks may involve waiting, such as I/O operations or network communication.
> [!info] Async Programming in Python
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> Asynchronous programming enables multiple operations to happen concurrently, using mechanisms such as callbacks, promises, or async/await keywords <a class="yt-timestamp" data-t="02:04:15">[02:04:15]</a>.
### asyncio Library
The `asyncio` library in Python provides an interface for writing single-threaded concurrent code using coroutines, allowing for efficient I/O-bound and high-level structured network code.
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> "We needed a better state-of-the-art module in the standard library for multiplexing input and output from different sources" — Guido van Rossum, reflecting on the need for the `asyncio` module <a class="yt-timestamp" data-t="02:06:59">[02:06:59]</a>.
### Use Case
Asynchronous programming is ideal for tasks that involve a significant amount of idle time, such as waiting for API responses or processing large datasets with disparate data fetching operations.
## Conclusion
While parallelism, concurrency, and asynchronous programming each serve to enhance program efficiency, their applications and implications differ significantly. Choosing the appropriate paradigm depends largely on the specific use case, whether it’s handling high I/O operations with asynchronous programming or distributing processor-heavy tasks across multiple cores with parallelism.