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| author | Charles Harris <charlesr.harris@gmail.com> | 2019-04-30 17:23:38 -0600 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2019-04-30 17:23:38 -0600 |
| commit | 374c4e031b7dad43513dcc6e7bbeb0cd76ad7cdb (patch) | |
| tree | 9919488b4c71757dd9494549a9ef38f9ab7ba1bd /doc | |
| parent | c51a56c4c78a409a884201004a1e7f605526a3a8 (diff) | |
| parent | 512c6451fd61c7d1e8053082cd93ecf10417ea22 (diff) | |
| download | numpy-374c4e031b7dad43513dcc6e7bbeb0cd76ad7cdb.tar.gz | |
Merge pull request #13413 from mattip/doc-linalg
DOC: document existence of linalg backends
Diffstat (limited to 'doc')
| -rw-r--r-- | doc/source/reference/routines.linalg.rst | 13 |
1 files changed, 13 insertions, 0 deletions
diff --git a/doc/source/reference/routines.linalg.rst b/doc/source/reference/routines.linalg.rst index c6bffc874..d42e77ad8 100644 --- a/doc/source/reference/routines.linalg.rst +++ b/doc/source/reference/routines.linalg.rst @@ -5,6 +5,19 @@ Linear algebra (:mod:`numpy.linalg`) ************************************ +The NumPy linear algebra functions rely on BLAS and LAPACK to provide efficient +low level implementations of standard linear algebra algorithms. Those +libraries may be provided by NumPy itself using C versions of a subset of their +reference implementations but, when possible, highly optimized libraries that +take advantage of specialized processor functionality are preferred. Examples +of such libraries are OpenBLAS_, MKL (TM), and ATLAS. Because those libraries +are multithreaded and processor dependent, environmental variables and external +packages such as threadpoolctl_ may be needed to control the number of threads +or specify the processor architecture. + +.. _OpenBLAS: https://www.openblas.net/ +.. _threadpoolctl: https://github.com/joblib/threadpoolctl + .. currentmodule:: numpy Matrix and vector products |