Writing Performant NumPy Code with Multi-Core CPUs — NumPy v2.6.dev0 Manual
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Using multi-core processors with Python standard libraries#
NumPy is designed for high performance numerical computing in Python by leveraging vectorized operations. However, vectorization does not always fully utilize the capabilities of multi-core processors. To exploit paralle
concurrent.futures.ProcessPoolExecutor
Use process pools to reuse existing processes instead of creating new ones for each task. concurrent.futures.ProcessPoolExecutor provides this feature.
Select appropriate startup methods. Avoid explicitly selecting fork unless you know that it is safe in your application. Forking a multithreaded process is problematic and can lead to deadlocks or crashes. Python 3.14 ch
Use shared memory constructs such as multiprocessing.shared_memory, multiprocessing.Array or multiprocessing.Value for large data that needs to be accessed by multiple processes.
Serialization of un-picklable objects
Consider third-party libraries such as joblib. joblib’s default backend loky relies on cloudpickle for serialization and can handle a wider range of Python objects than the standard pickle module. See the joblib document
Use joblib.cpu_count(), which takes into account constraints such as CPU affinity settings and Linux CFS scheduler quotas. (See joblib section for more details about joblib.)
https://docs.dask.org/en/stable/
Dask Documentation: https://docs.dask.org/en/stable/
https://joblib.readthedocs.io/en/latest/
joblib Documentation: https://joblib.readthedocs.io/en/latest/
threadpoolctl GitHub Repository: joblib/threadpoolctl