What is the difference between multithreading and multiprocessing?

Multithreading and multiprocessing are two approaches to parallel task execution, but they differ in level and organization method.

• Multithreading involves executing multiple threads within a single process. Threads share the process's memory and resources, which facilitates data exchange but requires synchronization to prevent conflicts. Multithreading is effective for tasks that need to perform several operations simultaneously with a shared state.

• Multiprocessing involves launching multiple independent processes, each with its own memory and resources. Processes are isolated from each other, increasing stability and security, but data exchange between them is more complex (via IPC — inter-process communication).

Example:

• In Python, the threading module implements multithreading, but due to GIL (Global Interpreter Lock), threads do not always provide a performance boost for CPU-intensive tasks.
• The multiprocessing module launches multiple processes, bypassing GIL and allowing efficient use of multiple CPU cores.

Thus, multithreading is better suited for I/O-intensive tasks, while multiprocessing is ideal for CPU-bound computations.

Multithreading is the execution of multiple threads within a single process, sharing memory and resources of the process. Multiprocessing is the execution of multiple processes, each with its own memory and resources, which can run in parallel on different CPU cores.

When to use:

• Multithreading is suitable for I/O-intensive tasks, where threads often wait for responses, and switching between them is efficient. It is also useful when you need to share state and data without the overhead of inter-process communication.
• Multiprocessing is better for CPU-intensive tasks, where multiple cores are used for parallel computations, avoiding GIL limitations (e.g., in Python). Processes are isolated, which enhances security and stability.

Example in Python:

import threading
import multiprocessing

def io_task():
    # simulate I/O operation
    pass

def cpu_task():
    # CPU-intensive operation
    pass

# Multithreading for I/O
threads = [threading.Thread(target=io_task) for _ in range(5)]
for t in threads:
    t.start()

# Multiprocessing for CPU
processes = [multiprocessing.Process(target=cpu_task) for _ in range(5)]
for p in processes:
    p.start()

Thus, the choice depends on the nature of the task and environment constraints.