This content originally appeared on DEV Community and was authored by Sushant Gaurav
Before going into the interview questions, if you want to learn about GIL in detail, click here.
Explain the GIL
It is a mutex in CPython that ensures only one thread executes Python bytecode at a time. It simplifies memory management (due to CPython’s non-thread-safe reference counting), but it prevents true parallel execution of Python threads in CPU-bound tasks.
When is GIL a problem?
It is a problem in the case of CPU-bound tasks (like heavy number crunching, image processing, or ML workloads). Even if there are multiple threads, the GIL forces them to run one at a time. So, no true parallel speedup.
It is not a problem for I/O-bound tasks (like networking, web scraping, or DB queries), since while one thread is waiting on I/O, another can acquire the GIL and run.
What is Thread safety, and how does the GIL work internally?
The GIL ensures thread safety by letting only one thread run Python code at once. Internally, the GIL is released during I/O operations and reacquired when execution resumes. CPython also forces context switches every few milliseconds to give each thread a chance to run.
Explain GIL vs Process-level parallelism?
With threads, the GIL blocks true parallelism for CPU-bound tasks. But with multiprocessing, each process has its own interpreter and GIL, so they run in true parallel across multiple CPU cores. This is why Python developers often prefer multiprocessing for CPU-heavy workloads.
What are the various workarounds of GIL?
- Multiprocessing: bypasses GIL by using multiple processes.
- C extensions (NumPy, TensorFlow, etc.): heavy lifting done in C/C++ where GIL is released.
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Async I/O (
asyncio): great for I/O-bound tasks, doesn’t need threading. - Alternative interpreters (Jython, IronPython): no GIL.
- Cython: allows writing Python-like code in C, and can release the GIL manually.
What is the difference between CPU-bound and I/O-bound tasks in the context of GIL?
CPU-bound: Heavy computation tasks that need CPU cycles (for example, prime number calculation). GIL becomes a bottleneck here.
I/O-bound: Tasks that spend time waiting for input/output (for example, network requests, file reads). GIL is not a big problem because threads can switch while waiting for I/O.
Why doesn’t multithreading speed up CPU-bound tasks in CPython?
Because the GIL forces only one thread to execute Python code at a time, threads end up taking turns instead of using multiple cores simultaneously.
How does Python achieve concurrency in I/O-bound tasks despite the GIL?
When a thread is waiting for I/O, Python releases the GIL, allowing another thread to run. This makes multithreading useful for I/O-heavy workloads.
How can you achieve true parallelism in Python?
By using the multiprocessing module, which spawns separate processes with their own Python interpreter and memory space, it bypasses the GIL.
What is the difference between multithreading and multiprocessing in Python with respect to the GIL?
- Multithreading: Shares memory, but is limited by the GIL (no parallel CPU execution).
- Multiprocessing: Each process has its own GIL, so tasks can run in parallel on multiple cores.
How does the GIL impact multi-core CPU utilisation?
It prevents full utilisation of multiple cores in CPU-bound tasks because only one thread can execute Python code at a time per process.
Does every Python implementation have a GIL? (for example, Jython, PyPy, IronPython)
No. The GIL is specific to CPython. For example, Jython and IronPython do not have a GIL because they rely on the JVM and .NET threading models. PyPy does have a GIL, though efforts exist to remove it.
How does CPython’s memory management contribute to the need for the GIL?
CPython uses reference counting for memory management. Without the GIL, updating reference counts across multiple threads would require fine-grained locks, which would slow things down.
Can the GIL be removed from Python? What are the challenges?
Yes, but it’s extremely difficult. Removing it would require:
- Adding fine-grained locks around memory operations.
- Risking performance regression in single-threaded programs (which are the majority).
That’s why the GIL is still there despite many attempts to remove it.
What happens if you run a CPU-heavy function with multiple threads in Python?
It will likely run slower than a single thread due to the GIL and thread-switching overhead.
When would you prefer multiprocessing over multithreading in Python?
- For CPU-bound tasks: use multiprocessing.
- For I/O-bound tasks: multithreading (or asyncio) is usually sufficient.
How does asyncio differ from threading when dealing with the GIL?
- Threading: Uses multiple OS threads. Still blocked by the GIL for CPU-bound tasks.
- Asyncio: Uses a single thread with an event loop, switching tasks cooperatively. It avoids GIL issues but is only good for I/O-bound, non-blocking workloads.
What are real-world scenarios where the GIL becomes a bottleneck?
- Scientific computing with pure Python loops.
- Machine learning preprocessing if not using optimised C libraries.
- Any workload needing parallel computation in Python code.
How do C extensions (like NumPy) bypass or work around the GIL?
C extensions can release the GIL while running heavy computations in native code. That’s why NumPy, TensorFlow, and Pandas can achieve parallelism internally.
Is the GIL considered a disadvantage of Python? Why or why not?
Yes, for CPU-bound multithreading workloads. But for single-threaded programs and I/O-bound tasks, it’s not a big issue. It also makes Python simpler and safer for developers.
How does the GIL interact with Python’s garbage collection (reference counting)?
The GIL ensures that increments/decrements of reference counts are atomic, preventing race conditions in memory management.
If you were designing Python today, would you keep or remove the GIL? Why?
- If optimising for performance and simplicity (especially single-threaded programs), it should be kept.
- If optimising for multi-core parallelism from the start, the design of the memory management should be different and should avoid the GIL.
This content originally appeared on DEV Community and was authored by Sushant Gaurav