The difference between multiprocessing and multithreading lies primarily in how concurrency is achieved, the resources used, and Python-specific constraints like the Global Interpreter Lock (GIL).
Related:
| Term | Description |
|---|---|
| Multiprocessing | Uses multiple processes, each with its own Python interpreter and memory space. |
| Multithreading | Uses multiple threads within a single process, sharing the same memory space. |
Key Differences:
| Aspect | Multiprocessing | Multithreading |
|---|---|---|
| Memory Space | Each process has its own memory. | Threads share the same memory space. |
| CPU Utilization | Can use multiple CPU cores fully. | Limited by GIL in CPython (only one thread runs Python bytecode at a time). |
| Overhead | Higher: more memory, process startup time. | Lower: threads are lightweight to spawn. |
| Data Sharing | Requires IPC (Inter-Process Communication) like Queues or Pipes. | Shared memory makes data sharing easier but riskier. |
| Best For | CPU-bound tasks (e.g. image processing, computation). | I/O-bound tasks (e.g. file/network access). |
| Crash Safety | One process crash doesn’t affect others. | A crash in one thread can crash the whole process. |
Use Case Summary:
| Use Case | Recommendation |
|---|---|
| CPU-intensive (e.g. matrix multiplication, image classification) | Multiprocessing |
| I/O-intensive (e.g. downloading files, database queries) | Multithreading |
| Parallelizing EasyOCR on multiple images | Multiprocessing |
| Handling many simultaneous HTTP requests | Multithreading or AsyncIO |