Is There a Thread-Based Equivalent to Python's Multiprocessing Pool?

A Thread-Based Pool: An Alternative to the Multiprocessing Pool

Multiprocessing in Python provides a powerful mechanism for parallel processing using multiple processes. The multiprocessing.Pool class offers a convenient interface for managing worker processes and distributing tasks. However, when heavyweight processes are undesirable, is there a similar solution that utilizes threads instead?

Yes, there is a hidden gem within the multiprocessing module that offers thread-based parallelism: the ThreadPool class. To access it, import it using:

from multiprocessing.pool import ThreadPool

The ThreadPool class wraps a dummy Process class that internally runs a Python thread. This approach allows for a thread-based multiprocessing API, similar to the standard Pool class. But unlike worker processes, threads share memory, potentially reducing overhead.

The use of this thread-based ThreadPool mirrors that of the standard Pool. For instance, to parallelize a map operation using threads:

def long_running_func(p):
    c_func_no_gil(p)

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

Note that in this scenario, the GIL is not a concern as the underlying function releases it before performing IO-bound operations. So, for IO-intensive tasks, the ThreadPool can provide a significant performance boost while avoiding the overhead of creating and managing processes.

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