Threading and multiprocessing — PythonCert 5.0 documentation

This resource introduces Python's `threading` and `multiprocessing` modules, explaining the concepts of parallel programming, common challenges like race conditions and deadlocks, and methods for synchronization using locks. It provides guidance on breaking down problems for parallel execution and implementing threading within custom classes.

Overview

Added

March 17, 2026

Subject & domain

computer-science-advanced · parallel-concurrent-computing

Grade range

Grade 9 (Freshman)–Grade 12 (Senior)

Page kind

Article

Introduction

Threading and Multiprocessing in Python

  • Core Concepts: Python provides threading and multiprocessing modules for concurrency. While they share similar APIs, they serve different purposes.
  • Threading Limitations:
    • Python threads are "true" OS-level threads, but there is no Python-specific thread scheduler.
    • I/O Bound: Excellent for I/O-bound tasks (can handle thousands of threads).
    • CPU Bound: Avoid using threads for CPU-bound tasks in Python, as the Global Interpreter Lock (GIL) can make them slower than sequential execution. Use C extensions that release the GIL for CPU-intensive work.
  • Synchronization Challenges:
    • Race Conditions: Occur when multiple threads access and modify shared data concurrently, leading to unpredictable results.
    • Deadlocks: Occur when threads wait indefinitely for each other to release resources.
    • Livelocks: Occur when threads repeatedly change state in response to each other without making progress.
  • Synchronization Tools:
    • Locks (threading.Lock): Implements mutual exclusion (mutex). Use context managers for safety.
    • RLock (Reentrant Lock): Allows the same thread to acquire the lock multiple times; useful for recursive algorithms.
    • Semaphores: Maintains a counter to limit the number of threads accessing a resource simultaneously.
    • Events: Used for signaling between threads (e.g., waiting for a specific state).
    • Conditions: Combines locking and signaling for complex producer/consumer patterns.
  • Thread Management:
    • Daemon Threads: Set via thread.daemon = True. These are terminated abruptly at program exit without cleanup; useful for background tasks like garbage collection.
    • Joining: Use thread.join() to block the main program until a specific thread completes.
    • Subclassing: You can create custom threads by subclassing threading.Thread and implementing the run() method.
  • Communication:
    • Queues (queue module): The preferred, thread-safe way to exchange data between threads.
    • Queue Types: Includes standard FIFO, LifoQueue (Last-In, First-Out), and PriorityQueue (retrieves lowest-valued entries first).
  • Scheduling:
    • threading.Timer: Executes a function after a specified delay.
    • sched module: A low-level module for scheduling events based on absolute time or delays.

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