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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:
Alternative Solutions:
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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