Link ATLAS/MKL to an installed Numpy

Question

TL;DR how to link ATLAS/MKL to existing Numpy without rebuilding.

I have used Numpy to calculate with the large matrix and I found that it is very slow because Numpy only use 1 core to do calculation. After doing a lot of search I figure that my Numpy does not link to some optimized library like ATLAS/MKL. Here is my config of numpy:

>>>import numpy as np >>>np.__config__.show() blas_info:     libraries = ['blas']     library_dirs = ['/usr/lib']     language = f77 lapack_info:     libraries = ['lapack']     library_dirs = ['/usr/lib']     language = f77 atlas_threads_info:     NOT AVAILABLE blas_opt_info:     libraries = ['blas']     library_dirs = ['/usr/lib']     language = f77     define_macros = [('NO_ATLAS_INFO', 1)] atlas_blas_threads_info:   NOT AVAILABLE openblas_info:   NOT AVAILABLE lapack_opt_info:     libraries = ['lapack', 'blas']     library_dirs = ['/usr/lib']     language = f77     define_macros = [('NO_ATLAS_INFO', 1)] atlas_info:   NOT AVAILABLE lapack_mkl_info:   NOT AVAILABLE blas_mkl_info:   NOT AVAILABLE atlas_blas_info:   NOT AVAILABLE mkl_info:   NOT AVAILABLE 

For this reason, I want to link ATLAS/MKL to Numpy. However, my Numpy is installed from PIP so I don't want to install manually because I want to use the latest version. I have done some search but they are only for building from scratch. For this reason, my question are:

• Are there any way to link ATLAS/MKL to Numpy without rebuilding again?
• I have found that the config info is saved in _config_.py in the installed folder of Numpy. So will modifying it solve my problem? If yes, would you please show me how?

Answer 1

Assuming you're running some flavour of linux, here's one way you could do it:

1. Find out what BLAS library numpy is currently linked against using ldd.

   • For versions of numpy older than v1.10:

     $ ldd /<path_to_site-packages>/numpy/core/_dotblas.so 

     For example, if I install numpy via apt-get, it links to

     ... libblas.so.3 => /usr/lib/libblas.so.3 (0x00007fed81de8000) ... 

     If _dotblas.so doesn't exist, this probably means that numpy failed to detect any BLAS libraries when it was originally installed, in which case it simply doesn't build any of the BLAS-dependent components. This often happens if you install numpy using pip without manually specifying a BLAS library (see below). I'm afraid you'll have no option but to rebuild numpy if you want to link against an external BLAS library.

   ---

   • For numpy v1.10 and newer:

     _dotblas.so has been removed from recent versions of numpy, but you should be able to check the dependencies of multiarray.so instead:

     $ ldd /<path_to_site-packages>/numpy/core/multiarray.so 

2. Install ATLAS/MKL/OpenBLAS if you haven't already. By the way, I would definitely recommend OpenBLAS over ATLAS - take a look at this answer (although the benchmarking data is now probably a bit out of date).

3. Use update-alternatives to create a symlink to the new BLAS library of your choice. For example, if you installed libopenblas.so into /opt/OpenBLAS/lib, you would do:

   $ sudo update-alternatives --install /usr/lib/libblas.so.3 \                                      libblas.so.3 \                                      /opt/OpenBLAS/lib/libopenblas.so \                                      50 

   You can have multiple symlinks configured for a single target library, allowing you to manually switch between multiple installed BLAS libraries.

   For example, when I call $ sudo update-alternatives --config libblas.so.3, I can choose between one of 3 libraries:

     Selection    Path                                    Priority   Status ------------------------------------------------------------   0            /opt/OpenBLAS/lib/libopenblas.so         40        auto mode   1            /opt/OpenBLAS/lib/libopenblas.so         40        manual mode   2            /usr/lib/atlas-base/atlas/libblas.so.3   35        manual mode * 3            /usr/lib/libblas/libblas.so.3            10        manual mode 

If you really want the "newest" version of numpy, you could also take a look at my answer on compiling numpy from source with OpenBLAS integration.

Installing numpy with BLAS support using pip

As @tndoan mentioned in the comments, it's possible to make pip respect a particular configuration for numpy by placing a config file in ~/.numpy-site.cfg - see this answer for more details.

My personal preference is to configure and build numpy by hand. It's not particularly difficult, and it gives you better control over numpy's configuration.