❔ Interview Question
What is the GIL (Global Interpreter Lock) in Python, and how does it impact the execution of multi-threaded programs?
Answer: The Global Interpreter Lock (GIL) is a mutex (or a lock) that allows only one thread to hold the control of the Python interpreter at any one time. This means that in a CPython process, only one thread can be executing Python bytecode at any given moment, even on a multi-core processor.
This has a significant impact on performance:
• For CPU-bound tasks: Multi-threaded Python programs see no performance gain from multiple CPU cores. If you have a task that performs heavy calculations (e.g., image processing, complex math), creating multiple threads will not make it run faster. The threads will execute sequentially, not in parallel, because they have to take turns acquiring the GIL.
• For I/O-bound tasks: The GIL is less of a problem. When a thread is waiting for Input/Output (I/O) operations (like waiting for a network response, reading from a file, or querying a database), it releases the GIL. This allows another thread to run. Therefore, the
How to achieve true parallelism?
To bypass the GIL and leverage multiple CPU cores for CPU-bound tasks, you must use the
tags: #Python #Interview #CodingInterview #GIL #Concurrency #Threading #Multiprocessing #SoftwareEngineering
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By: @DataScienceQ ✨
What is the GIL (Global Interpreter Lock) in Python, and how does it impact the execution of multi-threaded programs?
Answer: The Global Interpreter Lock (GIL) is a mutex (or a lock) that allows only one thread to hold the control of the Python interpreter at any one time. This means that in a CPython process, only one thread can be executing Python bytecode at any given moment, even on a multi-core processor.
This has a significant impact on performance:
• For CPU-bound tasks: Multi-threaded Python programs see no performance gain from multiple CPU cores. If you have a task that performs heavy calculations (e.g., image processing, complex math), creating multiple threads will not make it run faster. The threads will execute sequentially, not in parallel, because they have to take turns acquiring the GIL.
• For I/O-bound tasks: The GIL is less of a problem. When a thread is waiting for Input/Output (I/O) operations (like waiting for a network response, reading from a file, or querying a database), it releases the GIL. This allows another thread to run. Therefore, the
threading module is still highly effective for tasks that spend most of their time waiting, as it allows for concurrency.How to achieve true parallelism?
To bypass the GIL and leverage multiple CPU cores for CPU-bound tasks, you must use the
multiprocessing module. It creates separate processes, each with its own Python interpreter and memory space, so the GIL of one process does not affect the others.tags: #Python #Interview #CodingInterview #GIL #Concurrency #Threading #Multiprocessing #SoftwareEngineering
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By: @DataScienceQ ✨
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