What is the difference between multi-threading and multi-processing?

Multi‑threading and multi‑processing are two ways to achieve concurrency. In multi‑threading, several threads run within the same process and share the same memory space. This allows fast communication but requires careful synchronization to avoid race conditions. In Python, the Global Interpreter Lock (GIL) means that only one thread executes Python bytecode at a time, so CPU‑bound tasks do not run in parallel; however, I/O‑bound tasks can benefit because threads can wait on I/O while others run.

Multi‑processing creates separate processes, each with its own memory space and its own Python interpreter. Because processes do not share memory, they can truly run in parallel on multiple CPU cores, making them suitable for CPU‑bound workloads. The trade‑off is higher overhead for process creation and inter‑process communication (IPC) via pipes, queues, or shared memory. In summary, use threads for I/O‑bound or lightweight parallelism where shared state is needed, and use processes for CPU‑bound tasks that require true parallel execution.