Multithreading and multiprocessing are two techniques used to achieve concurrent execution in programs, but they differ in approach, resource usage, and applications.

Key Differences Definition:

Multithreading: Multiple threads of a single process execute concurrently. Threads share the same memory space, making them lightweight. Multiprocessing: Multiple processes execute concurrently. Each process has its own memory space, making them independent but resource-intensive. Execution:

Multithreading: Ideal for I/O-bound tasks where the program waits for external operations like reading a file or network data. Multiprocessing: Suited for CPU-bound tasks that require heavy computation, like image processing or large-scale calculations. Resource Usage:

Multithreading: Shares resources, which reduces overhead but can lead to issues like race conditions. Multiprocessing: Uses separate memory and resources, making it safer but with higher overhead. Performance:

Multithreading: Limited by the Global Interpreter Lock (GIL) in languages like Python. Multiprocessing: Exploits multiple CPUs/cores for true parallelism. Example in Python: Threading: threading module handles tasks like downloading files concurrently. Multiprocessing: multiprocessing module leverages multiple cores for heavy computations. Results: Use multithreading for tasks needing quick context switches and shared memory. Opt for multiprocessing for compute-heavy tasks requiring true parallelism. Choose based on your program’s nature and resource availability.