How to Restrict Thread Count in NumPy for Optimal Performance?

Limiting Thread Count in NumPy

NumPy utilizes multi-CPU libraries like BLAS for efficient numerical computations. However, you may encounter instances where the default thread count (4) proves excessive. To address this, you can leverage specific environment flags.

Before executing the NumPy script, consider setting the following flags:

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

The MKL_NUM_THREADS flag sets the allowed thread count for the Intel Math Kernel Library (MKL). NUMEXPR_NUM_THREADS controls NumExpr's threading, which is used for fast numerical computations. Finally, OMP_NUM_THREADS manages the thread count utilized by OpenMP, an industry-standard for parallel programming in C/C /Fortran.

Setting these flags ensures that all libraries within NumPy adhere to a strict thread limit of 1. This can enhance performance for matrix multiplication and other operations that do not benefit from multiple threads.

Note that if you encounter issues despite setting the mentioned flags, it may indicate that multithreading is introduced elsewhere in the codebase. In such cases, additional investigation is warranted.

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