NumPy has a few import-time, compile-time, or runtime configuration
options which change the global behaviour. Most of these are related to
performance or for debugging purposes and will not be interesting to the
vast majority of users.
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
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.
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.
Changed in version 1.25.2: This variable is only checked on the first import.
