Before we dive into multithreading and multiprocessing, let’s first cover some background info on concurrency, parallelism and asynchronous tasks. These three concepts are related but distinct.Let’s use a simple example to understand them: a mechanics shop. Concurrency happens when one mechanic works on several cars by switching between them. For example, the mechanic changes the oil in one car while waiting for a part for another. They don’t finish one car before starting the next, but they can’t do two tasks at exactly the same time. The tasks overlap in time but don’t happen simultaneously.Parallelism occurs when several mechanics work on different cars at the same time. One mechanic changes the oil, another replaces the brakes and another installs a battery. These tasks happen simultaneously, and can be completed at different times or at the same time because different people handle them.Asynchronous tasks work like this: You drop your car off and get a text when it is done. While the work takes place, you can do whatever you want: Go to work, the beach or do anything else, and the work still gets done in the background.Now to the tech explanations.The quick answer: Async tasks use concurrency, but not all concurrency is async. Parallelism stands apart and mainly relates to CPU usage. All parallelism involves concurrency, but not all concurrency achieves parallelism. Confusing? Let’s keep going!The long answer: Asynchronous tasks form a type of concurrency, usually for I/O-bound work. Tasks yield control while waiting, for example, for a file or network response. This allows other tasks to run. Concurrency covers running multiple tasks during the same period using threads, coroutines (async/await) or other methods. Parallelism means actually doing tasks simultaneously on multiple CPU cores. This approach fits CPU-bound work and often uses multiprocessing.Why does this matter to Python programmers?Multiprocessing and Multithreading: Definitions and UsesMultithreading (concurrency) lets a single program create multiple threads sharing the same memory space. These threads run concurrently, switching between tasks to make progress. Multithreading works well for I/O-bound tasks. However, because of Python’s Global Interpreter Lock (GIL), threads cannot run Python bytecode in true parallel on multiple CPU cores.Multiprocessing (parallelism) runs multiple processes independently. Each process has its own memory space and Python interpreter. This setup enables true parallelism by running tasks simultaneously on multiple CPU cores. Multiprocessing suits CPU-bound tasks because it bypasses the GIL limitation.Deciding between thread pools and process pools depends on your task type — whether it’s I/O-bound or CPU-bound.Why Do We Need Multithreading and Multiprocessing? Meet Python’s GILThe GIL is a mechanism in CPython, the standard Python implementation. It allows only one thread to execute Python bytecode at a time, even on multicore processors. Python introduced the GIL to simplify memory management and ensure thread safety.Because the GIL limits thread execution to one thread at a time:Threads cannot run Python bytecode in parallel, which reduces their effectiveness for CPU-bound tasks that require constant computation.This means:When you use multithreading, I/O-bound tasks can run efficiently because threads spend most of their time waiting on external resources like files, databases or network responses. The GIL only blocks actual Python bytecode execution.Multiprocessing enables true parallelism because each process has its own Python interpreter and GIL. This setup lets CPU-intensive tasks run across multiple cores.The GIL is why you use multithreading and multiprocessing to improve performance and responsiveness in Python applications when multiple tasks must execute at the same time.SyntaxPython offers several ways to handle concurrency and parallelism. Two of the most common are ThreadPoolExecutor and ProcessPoolExecutor. Both provide powerful abstractions that simplify complex concurrency management. These tools come from the concurrent.futures module. They help you manage async tasks efficiently.ThreadPoolExecutor and ProcessPoolExecutor share the same interface. This similarity makes switching between them easy with minimal code changes. Using the with statement ensures threads or processes clean up properly when you finish.What Is Python’s ThreadPool?Definition and How It WorksThreadPoolExecutor acts like the mechanic quickly switching between multiple cars on a task-by-task basis. ThreadPoolExecutor executes tasks concurrently using threads within the same Python process. Because threads share memory, they create and switch quickly.When To Use ThreadPoolExecutorUse a thread pool when your application spends a lot of time on tasks that don’t need much CPU. Tasks that involve waiting suit thread pools best since threads can run while others wait. Examples include:HTTP requestsReading and writing to diskDatabase queriesWhen you need to wait for user inputExample: Using ThreadPoolExecutor for I/O-bound TasksThis example simulates fetching data from multiple URLs concurrently. Each fetch_data call waits for 2 seconds, but threads make the overall time much shorter than running sequentially.View the code on Gist.What Is Python Multiprocessing?Definition and How It WorksProcessPoolExecutor (multiprocessing) acts like having multiple mechanics, each working on their own car at the same time. Multiprocessing runs tasks in separate processes, each with its own memory space and Python interpreter. This allows true parallel execution on multiple CPU cores. Unlike threads, multiple mechanics work simultaneously without waiting.When To Use MultiprocessingMultiprocessing excels at tasks that you can spread across multiple cores. Use a process pool for tasks that consume a lot of CPU. These tasks might:Perform mathematical computationsProcess large datasetsProcess images and videosExample: Using ProcessPoolExecutor for CPU-Bound TasksIn the example below, each factorial calculation requires heavy CPU work. Running them in separate processes lets Python bypass the GIL and use multiple cores.View the code on Gist.Key Differences Between ThreadPool and MultiprocessingLet’s evaluate thread pools and multiprocessing based on important criteria to help you choose the right approach.Parallelism: Threads offer concurrency but not true parallelism due to the GIL. Processes provide real parallel execution.Memory sharing: Threads share data easily. Processes cannot share memory directly.Startup time: Threads start faster. Processes take longer due to the overhead of creating a new interpreter.Best use cases: Threads fit I/O-bound tasks like downloading files or accessing APIs. Processes fit CPU-bound tasks like number crunching or data transformation.When To Use ThreadPoolExecutor vs. ProcessPoolExecutorChoose ThreadPoolExecutor when:Your tasks involve a lot of waiting.You need lightweight concurrency.Memory sharing matters.Choose ProcessPoolExecutor when:Tasks require heavy CPU work.You want to use multiple CPU cores.You do not need to share memory between tasks.Performance Considerations and BenchmarksMeasuring Execution Time for I/O-Bound TasksIn this example, we simulate I/O-bound tasks by sleeping for a few seconds. Using ThreadPoolExecutor, each task runs in its own thread. Threads overlap while waiting, so the overall runtime is much shorter than running tasks one after the other. This shows how threading improves performance for I/O-bound workloads.The output will be the amount of time it takes for your system to complete this task.Measuring Execution Time for CPU-Bound TasksHere, the function performs heavy arithmetic to simulate a CPU-bound task. These operations benefit from true parallelism. Using ProcessPoolExecutor, each task runs in a separate process on its own core if available. Processes bypass Python’s GIL, so they can execute Python bytecode in parallel. This reduces total runtime and improves efficiency for CPU-heavy workloads.The output will be the amount of time it takes for your system to complete this task.Common Pitfalls and Best PracticesAvoiding Deadlocks in ThreadPoolA deadlock happens when two or more threads wait for each other to release resources, causing all to stop progressing. Think of two mechanics holding tools that the other needs and refusing to let go. Work comes to a halt.You can avoid deadlocks by:Never blocking a thread indefinitely or waiting for it to completeUsing the with context to ensure threads join automaticallyBeing cautious with shared resources and protecting them with locks if neededHandling Memory Overhead in MultiprocessingEach process has its own memory space, so data does not share directly between processes. This separation increases memory overhead because large data structures duplicate across processes and consume more system memory.To manage memory:Avoid passing large data structures between processes when possible.Use tools like multiprocessing.Manager or shared memory constructs if you must share data to reduce duplication and memory use.Debugging ThreadPool and Multiprocessing IssuesDebugging concurrency and parallelism poses challenges because threads and processes run independently and often at the same time. Issues like race conditions or hidden errors become harder to detect.Here are some tips:Thread issues, such as race conditions, can prove tricky to reproduce and fix. Use synchronization tools like threading.Lock() to control access to shared resources.Process issues might not show standard tracebacks in the main program. Use multiprocessing.get_logger() or add logging inside subprocesses to capture errors.For complex debugging and performance analysis, use profiling tools or higher-level frameworks like joblib or dask that offer better abstractions and diagnostics.ConclusionUnderstanding the differences between concurrency, parallelism and asynchronous tasks helps you determine which approach your task requires. In Python, ThreadPoolExecutor and ProcessPoolExecutor give you simple ways to handle threading and multiprocessing for I/O-bound and CPU-bound tasks efficiently. Knowing when to use each can save time, reduce complexity and improve performance.The post Python ThreadPool vs. Multiprocessing appeared first on The New Stack.