How does Python’s GIL (Global Interpreter Lock) affect multithreading in AI/ML applications?
How does Python’s GIL (Global Interpreter Lock) affect multithreading in AI/ML applications?
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21-Apr-2025
Updated on 23-Apr-2025
Khushi Singh
23-Apr-2025The Global Interpreter Lock (GIL) in Python operates as a crucial element that determines multithreading operations within AI and machine learning (ML) applications. The GIL operates as a mutex inside CPython which serves to lock access of Python bytecode to a single executing thread. The principal objective behind this practice is to establish memory safety since the fundamental thread security features are absent in Python.
During AI/ML applications the system restriction of Global Interpreter Lock limits CPU performs on tasks like model training and mathematical computations and data processing operations. Multiple thread creation does not lead to simultaneous execution between different cores because of the GIL implementation which hinders multithreading performance. The problem becomes especially evident during CPU-intensive operation parallelization because threads regularly await access to the interpreter.
Operation systems tend to impact I/O-bound tasks like dataset loading or API calls or file reading with minimal delay. Thread execution achieves better concurrency during operations since the GIL releases to permit additional threads to proceed. AI/ML developers overcome CPU-bound tasks limitations by using the multiprocessing module because multithreading is not adequate with the GIL issue. Multiprocessing releases several processes that include individual Python interpreters and GILs which enable multi-core CPU-based parallel execution.
Numerous widely used AI/ML libraries function through C/C++ and native code to execute operations in optimized form. Performance-critical library operations release the GIL which enables multiple cores to work efficiently and results in similar parallel performance. The performance limitations of the GIL in Python do not affect AI workloads because multiprocessing enables parallel execution and native extensions operate without GIL constraints thereby delivering optimal performance across most practical applications.