What are the best practices for parallelism in Python?
An example of an AI chatbot conversation about best practices in Python parallelism.
What are the best practices for parallelism in Python?
When using parallelism in Python, consider using the multiprocessing module for CPU-bound tasks and concurrent.futures for easier thread management.
Can you explain the difference between multiprocessing and multithreading?
Sure! Multiprocessing involves multiple processes with separate memory spaces, ideal for CPU-bound tasks, while multithreading uses threads within a single process, suitable for I/O-bound tasks.
What are some common pitfalls to avoid when implementing parallelism?
Common pitfalls include race conditions, deadlocks, and excessive context switching. Always ensure thread safety and manage resources wisely.
How can I measure the performance of my parallel code?
You can use the time module to measure execution time or profiling tools like cProfile to analyze performance bottlenecks.
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