Multithreaded SciPy/NumPy with OpenBLAS on debian

Some months ago, just after I got an 8-core CPU, I wasted a weekend trying to get SciPy/NumPy to build against OpenBLAS. OpenBLAS is neat, as it has built-in and automatic support for multi-threading, for things like computing the dot-matrix of large matrices this can really be a time saver.

I was using roughly these instructions, but it was too complicated and I got nowhere and gave up.

Then I got a new MacBook, and set it up using homebrew rather than macports, and I noticed that NumPy was built against OpenBLAS by default. Now, it would be a shame to let debian be any worse off…

Luckily, it was much easier than I thought:

1. Install the openblas and lapack libraries and dev-headers

sudo apt-get install libopenblas-dev liblapack-dev

2. Setup a virtualenv

To make sure we do not mess up the whole system, setup a virtualenv (if you ever install more than 3 python packages, and do not yet know about virtualenv, you really should get to know it, it’s a little piece of magic!):

virtualenv env
source env/bin/activate

3. Install NumPy

In Debian, openblas/lapack fit into the alternatives system, and the implementation you chose gets symlinked to /usr/lib, however, this confuses numpy and you must point it to the right place, i.e. to /usr/lib/openblas-base
Download and unpack NumPy: 

mkdir evn/download
pip install -d env/download numpy
mkdir env/build
cd env/build
tar xf ../download/numpy-1.7.1.tar.gz

Now create a site.cfg file with the following content: 

[default]
library_dirs= /usr/lib/openblas-base

[atlas]
atlas_libs = openblas

Build/install NumPy: 

python setup.py install

You can now check the file env/lib/python2.7/site-packages/numpy/__config__.py to make sure it found the right libs, mine looks like this: 

lapack_info={'libraries': ['lapack'], 
    'library_dirs': ['/usr/lib'], 'language': 'f77'}
atlas_threads_info={'libraries': ['openblas'], 
    'library_dirs': ['/usr/local/lib'], 
    'language': 'c', 
    'define_macros': [('ATLAS_WITHOUT_LAPACK', None)], 
    'include_dirs': ['/usr/local/include']}
blas_opt_info={'libraries': ['openblas'], 
    'library_dirs': ['/usr/local/lib'], 
    'language': 'c', 
    'define_macros': [('ATLAS_INFO', '"\\"None\\""')], 
    'include_dirs': ['/usr/local/include']}

4. Install SciPy

If NumPy installs cleanly, SciPy can simple be installed with pip: 

pip install scipy

5. Test!

Using these scripts you can test your NumPy and SciPy installation. Be activating/deactivating the virtualenv, you can test with/without OpenBLAS. If you have several CPU cores, you can see that with OpenBLAS up to 4 CPUs should also be used.

Without OpenBLAS I get: 

NumPy 
dot: 0.901498508453 sec

SciPy
cholesky: 0.11981959343 sec
svd: 3.64697360992 sec

with OpenBLAS:

NumPy
dot: 0.0569217920303 sec

SciPy
cholesky: 0.0204758167267 sec
svd: 0.81153883934 sec

Posted by at 7:47 am on July 23rd, 2013. 3 comments... »
Categories: Python, Statistics.

3 comments.

  1. Thanks much, but the DEFAULT in the site.cfg should be capitalized, e.g. ‘[DEFAULT]’ instead of ‘[default]’

    Posted by Leo on January 14th, 2015.

  2. That was very helpful — indirectly: I used a blog from Lee Mendelowitz (github) who actually linked to your blog.

    Posted by justin m on February 5th, 2015.

  3. ‘mkdir evn/download’ should be ‘mkdir env/download’.

    Posted by Wilfred on August 5th, 2015.

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