Global State

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.

Relaxed Strides Checking

The compile-time environment variables:

NPY_RELAXED_STRIDES_DEBUG=0
NPY_RELAXED_STRIDES_CHECKING=1

control how NumPy reports contiguity for arrays. The default that it is enabled and the debug mode is disabled. This setting should always be enabled. Setting the debug option can be interesting for testing code written in C which iterates through arrays that may or may not be contiguous in memory. Most users will have no reason to change these; for details see the memory layout documentation.

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.