How to Limit Numpy\'s Thread Utilization When OMP_NUM_THREADS=1 Fails?

Limit Number of Threads in Numpy

Despite attempts to set OMP_NUM_THREADS=1, in certain situations, Numpy may persist in utilizing more than one thread. To determine the root of this issue and achieve a desired thread count of 1 for matrix multiplication, let's delve into the environment variables that govern thread usage.

Numpy's configuration, retrievable through numpy.show_config(), reveals the use of the BLAS (Basic Linear Algebra Subroutines) library, which serves as a computational engine. However, identifying the specific BLAS implementation used by Numpy can be challenging.

To restrict the number of threads employed in BLAS, we can set environment variables before executing the script. Commonly used libraries include OpenBLAS, MKL (Intel Math Kernel Library), and NUMEXPR:

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

Remember, it's crucial to set these variables before running the script. If multiple threading still persists, it may indicate that multithreading is introduced from a different source, such as an additional library or code in your script.

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