multiprocessingPool

The multiprocessingPool, commonly referred to via multiprocessing.Pool in Python, is a class in the standard library that provides a pool of worker processes to execute tasks concurrently. It enables data-parallel execution across multiple CPU cores, helping to speed up CPU-bound workloads by bypassing the Global Interpreter Lock in CPython.

A pool is created with parameters such as processes, initializer, initargs, and maxtasksperchild. If processes is None, the number of worker processes defaults to the number of CPU cores. The pool’s lifecycle can be managed with a context manager (with Pool(...) as pool:) to ensure automatic closing and joining, or by explicitly calling close() and join() when finished.

Tasks are submitted through a variety of methods, including map, map_async, apply, apply_async, starmap, imap, and imap_unordered. These methods can be blocking or return AsyncResult objects, from which results are retrieved via get() or by iterating over imap variants. The pool distributes work among the worker processes and collects results in a coordinated fashion.

Key considerations include the requirement that functions and arguments passed to the pool be pickleable so they can be sent to worker processes. On Windows, code that starts a new process should be guarded with if __name__ == '__main__' to avoid recursive process spawning. Memory usage scales with the number of worker processes, and inter-process communication incurs overhead; thus, multiprocessing is best suited for substantial CPU-bound tasks rather than small, frequent calls.

Alternatives and related tools include concurrent.futures.ProcessPoolExecutor for a modern interface, or ThreadPoolExecutor for IO-bound workloads where parallel threads may be more appropriate. The multiprocessingPool is a foundational tool for parallel execution in Python when true multi-core utilization is required.