Quick Answer
Python multiprocessing: multiprocessing.Pool for parallel task execution across CPU cores. pool.map(func, iterable) for parallel map. pool.starmap() for multiple arguments. ProcessPoolExecutor (concurrent.futures) is a cleaner API. Share data with multiprocessing.Queue, Pipe, or shared memory (multiprocessing.shared_memory in Python 3.8+). Spawn vs fork: spawn is safer on macOS/Windows (default), fork is faster on Linux.
Answer
Use ProcessPoolExecutor for CPU-bound tasks. Distribute workloads across processes. Avoid shared state unless using managers or queues.
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SugharaIQ Editorial Team
Verified Answer
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