“GIL” can impact performance in multithreaded applications from "summary" of Fluent Python by Luciano Ramalho
The Global Interpreter Lock (GIL) in Python is a mutex that protects access to Python objects, preventing multiple threads from executing Python bytecodes simultaneously. As a result, only one thread can execute Python bytecodes at a time, even on multi-core systems. This limitation can impact the performance of multithreaded applications, as threads are unable to take full advantage of multiple cores due to the GIL. The GIL is necessary because CPython, the reference implementation of Python, is not thread-safe at the C level. Without the GIL, multiple threads could access and modify Python objects concurrently, leading to data corruption and crashes. By enforcing the GIL, CPython ensures that only one thread executes Python bytecodes at a time, maintaining the integrity of Python objects. While the GIL simplifies the implementation of CPython, it can be a bottleneck for multithreaded applications that perform CPU-bound tasks. Since only one thread can execute Python bytecodes at a time, the GIL effectively serializes the execution of threads, limiting the parallelism that can be achieved. As a result, CPU-bound threads may not see significant performance improvements when running on multiple cores. However, the GIL does not prevent multithreaded applications from benefiting from concurrency when performing I/O-bound tasks. In such cases, threads may spend most of their time waiting for I/O operations to complete, during which the GIL is released. This allows other threads to execute Python bytecodes, making I/O-bound tasks well-suited for multithreading in Python despite the presence of the GIL. To overcome the limitations imposed by the GIL, developers can use multiprocessing instead of multithreading for CPU-bound tasks. By creating separate processes, each with its own Python interpreter and memory space, developers can bypass the GIL and take full advantage of multiple cores. While multiprocessing incurs higher overhead than multithreading due to inter-process communication, it offers a way to achieve parallelism in Python without being hindered by the GIL.
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