When Should I Use Python's Undocumented `ThreadPool` for Parallel Processing?

Thread-Based Pooling in Multiprocessing

The multiprocessing module offers a powerful "Pool" class for parallelizing tasks using separate processes. However, this approach incurs overhead due to process creation. For IO-bound operations with the GIL released during the actual function call, using threads can yield better performance.

Introducing the ThreadPool Class

Contrary to popular belief, the multiprocessing module does indeed provide a thread-based pool interface. This hidden gem, accessible via from multiprocessing.pool import ThreadPool, offers a convenient way to parallelize tasks using threads.

Despite its undocumented status, the ThreadPool class implements the multiprocessing pool interface using a dummy Process class that wraps python threads. This dummy Process class resides in the multiprocessing.dummy module, which offers the entire multiprocessing interface based on threads.

Example Usage

Similar to the ProcessPool, the ThreadPool can be used to parallelize map functions:

import multiprocessing.pool

def long_running_func(p):
    c_func_no_gil(p)

p = multiprocessing.pool.ThreadPool(4)
xs = p.map(long_running_func, range(100))

Note: The ThreadPool class is not as efficient as the ProcessPool for all cases, especially when tasks require significant CPU time.

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