Why Does Importing Numpy Limit Multiprocessing to a Single Core?

Impact of Numpy Import on Multiprocessing Core Assignment

Introduction:

Multiprocessing, a Python library for parallelizing tasks, aims to distribute work across multiple cores. However, users have encountered an issue where Numpy's import interferes with this distribution, resulting in all worker processes being assigned to a single core.

Explanation:

Upon importing Numpy, certain CPU-intensive modules within Numpy (e.g., OpenBLAS) can modify core affinity. This interference assigns all worker processes to the same core, eliminating the parallelization benefits of multiprocessing.

Workaround:

To resolve this issue, reset the task affinity using the code snippet: os.system("taskset -p 0xff %d" % os.getpid()). This command forces the operating system to distribute worker processes evenly across all available cores.

Additional Considerations:

• This approach has been observed to have no significant impact on Numpy's performance, but the effects may vary depending on specific machines and tasks.

Alternative Solutions:

• Set the environment variable OPENBLAS_MAIN_FREE=1 before running the script.
• During compilation, modify the OpenBLAS Makefile.rule to include NO_AFFINITY=1.

By applying these solutions, multiprocessing can effectively distribute worker processes across multiple cores, resolving the initial issue of core clustering and enhancing parallelization performance.

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