How to Limit Thread Usage in NumPy for Optimal Performance?

Customizing Thread Usage in NumPy

NumPy employs multiple threads for various operations, which can impact performance on multi-core systems. To limit the number of threads utilized by NumPy, there are several environment variables that can be set.

The environment variables that control thread usage in NumPy may differ depending on the underlying linear algebra library being used, such as BLAS. To identify the library in use, you can use the numpy.show_config() function.

If your Numpy configuration reveals that it is utilizing BLAS, you can try setting the following environment variables before executing your script:

• export MKL_NUM_THREADS=1
• export NUMEXPR_NUM_THREADS=1
• export OMP_NUM_THREADS=1

These settings should limit the number of threads used for matrix multiplication and other computations in NumPy.

Keep in mind that determining the exact source of multithreading in your code can be challenging. If the above variables do not resolve the issue, it is worth exploring other library-specific environment flags that may affect threading behavior.

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