What are the limitations of parallel programming in Python?
An example of an AI chatbot conversation about the limitations of parallel programming in Python.
What are the main limitations of parallel programming in Python?
Some main limitations include the Global Interpreter Lock (GIL), which can hinder true parallelism, and the complexity of managing shared state.
How does the GIL affect performance in multi-threaded applications?
The GIL allows only one thread to execute at a time in a single process, which can lead to performance bottlenecks in CPU-bound tasks.
Are there any libraries that can help with parallel programming in Python?
Yes, libraries like multiprocessing, concurrent.futures, and joblib can help you implement parallelism more effectively.
What are some best practices for parallel programming in Python?
Best practices include minimizing shared state, using process-based parallelism for CPU-bound tasks, and leveraging asynchronous programming for I/O-bound tasks.
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