Global Configuration Options#

Number of threads used for linear algebra#

NumPy itself is normally intentionally limited to a single thread during function calls, however it does support multiple Python threads running at the same time. Note that for performant linear algebra NumPy uses a BLAS backend such as OpenBLAS or MKL, which may use multiple threads that may be controlled by environment variables such as OMP_NUM_THREADS depending on what is used. One way to control the number of threads is the package threadpoolctl

madvise hugepage on Linux#

When working with very large arrays on modern Linux kernels, you can experience a significant speedup when transparent hugepage is used. The current system policy for transparent hugepages can be seen by:

cat /sys/kernel/mm/transparent_hugepage/enabled

When set to madvise NumPy will typically use hugepages for a performance boost. This behaviour can be modified by setting the environment variable:

NUMPY_MADVISE_HUGEPAGE=0

or setting it to 1 to always enable it. When not set, the default is to use madvise on Kernels 4.6 and newer. These kernels presumably experience a large speedup with hugepage support. This flag is checked at import time.

SIMD feature selection#

Setting NPY_DISABLE_CPU_FEATURES will exclude simd features at runtime. See Runtime Dispatch.

Warn if no memory allocation policy when deallocating data#

Some users might pass ownership of the data pointer to the ndarray by setting the OWNDATA flag. If they do this without setting (manually) a memory allocation policy, the default will be to call free. If NUMPY_WARN_IF_NO_MEM_POLICY is set to "1", a RuntimeWarning will be emitted. A better alternative is to use a PyCapsule with a deallocator and set the ndarray.base.