Building numpy and scipy with Intel compilers and Intel MKL on a 64 bit machine

At work we make heavy use of the Python/numpy/scipy stack. We have built numpy and scipy against Intel’s MKL, with Intel’s C++ compiler icc and Intel’s Fortran compiler ifort. As far as I know, and also from my own experience, this combination of tools lets you get close-to-optimum performance for numerical simulations on classical hardware, especially for linear algebra calculations. If you get the build right, and write proper code, i.e. use numpy’s data types and numpy’s/scipy’s functions the right way, then — spoken in general terms — no commercial or other open source software package is able to squeeze more performance from your hardware. I just updated the installation to the most recent Python 2 (2.7.6), numpy 1.8.0 and scipy 0.13.3. I built on a 64 bit machine with the Intel suite version 12.1.3 — newer versions of the Intel suite have troubles with respect to resolving library dependencies (which is the topic of this blog post). The documentation on the topic of building numpy and scipy with the Intel suite is sparse, so I describe the procedure I took in this article.

A few notes on performance and version decisions: compiling numpy and scipy against MKL provides a significant single-thread performance boost over classical builds. Furthermore, building against Intel’s OpenMP library can provide extremely efficient multicore performance through automatic threading of normal math operations. These performance boosts are significant. You want this boost, believe me, so it makes a lot of sense to build numpy/scipy with the Intel suite. However, it does not really make sense to build (C)Python with the Intel suite. It is not worth the effort — somewhere I have read that it might even be slower than an optimized GCC build. In any case, Python is not so much about math. It is about reliability and control. We should do math in numpy/scipy and use optimized builds for them — then there simply is no significance in optimizing the Python build itself. Regarding the Intel suite, I am pretty sure that newer versions do not provide large performance improvements compared to the 12.1.3 one. These reasons made me build things the following way:

  • CPython 2.7.6 in classical configure/make/make install fashion, with the system’s GCC.
  • numpy and scipy with Intel suite (MKL, icc, ifort) version 12.1.3.


I assume that you have built Python as a non-privileged user and installed it to some location in your file system (btw: never just overwrite your system’s Python with a custom build!). Set up the environment for this build, so that

$ python

invokes it. You can validate this via $ python --version and $ command -v python. In the following steps, we will work with the same user used for building and installing Python: numpy and scipy installation files will go right into the directory tree of the custom Python build.

I also assume that you have a working copy of MKL, icc, ifort, and that you have set it up like this:

$ source /path_to_intel_compilers/bin/ intel64

After invoking, your PATH and LD_LIBRARY_PATH environment variables should contain the directories where all the Intel binaries reside. A simple test (which you also should perform):

$ icc --version
icc (ICC) 12.1.3 20120212

Prepare, build, install, and validate numpy

In the numpy source directory create the file site.cfg with the following content:

library_dirs = /path_to_intel_compilers/mkl/lib/intel64/
include_dirs = /path_to_intel_compilers/mkl/include/
mkl_libs = mkl_rt
lapack_libs =

That is right, no more contents are needed in that file, it can be that simple. This approach is also recommended in

What is left to be done is setting compiler flags. The build process uses compiler abstractions stored in the two files: numpy/distutils/fcompiler/ and numpy/distutils/ You might feel the need to treat these files with respect, because they appear to be so important. I am frightened of these files, because they are partly inconsistent in themselves and have a quite bloaty appearance not justified by the very simple purpose I expect them to serve. My point is: do not be afraid, and make some drastic reductions in order to be sure about what happens during the build. In numpy/distutils/fcompiler/ you can safely edit the get_flags* methods of the IntelEM64TFCompilerclass, so that they look like this:

class IntelEM64TFCompiler(IntelFCompiler):
    compiler_type = 'intelem'
    def get_flags(self):
        return ['-O3 -g -xhost -openmp -fp-model strict -fPIC']
    def get_flags_opt(self):
        return []
    def get_flags_arch(self):
        return []

See, this compiler class describes itself as intelem and that is the compiler type we are going to use in the build command line. The “IntelEM64TFCompiler” has no meaning at all. We are just editing one of the classes, make sure that it has the compiler flags we want and eventually use this compiler abstraction during build. In the code above, I have made sure that the compiler flags '-O3 -g -xhost -openmp -fp-model strict -fPIC' are used by making the method get_flags return them, and make all other related methods return “nothing”. Additionally (not shown above), I have also edited the possible_executables attribute of the class:

possible_executables = ['ifort']

just to make sure that ifort will be used. I have also removed all compiler classes not needed from that file, just to get a better overview.

With the above’s steps, the Fortran compiler has been set up. Next, edit numpy/distutils/ for configuring the C compiler. I have deleted tons of stuff from this file. What follows is all the content remaining (you can copy/paste it like this, I think, this should be safe for most purposes):

from __future__ import division, absolute_import, print_function
from distutils.unixccompiler import UnixCCompiler
from numpy.distutils.exec_command import find_executable
class IntelEM64TCCompiler(UnixCCompiler):
    """ A modified Intel x86_64 compiler compatible with a 64bit gcc built Python.
    compiler_type = 'intelem'
    cc_exe = 'icc -O3 -g -fPIC -fp-model strict -fomit-frame-pointer -openmp -xhost'
    #cc_args = "-fPIC"
    def __init__ (self, verbose=0, dry_run=0, force=0):
        UnixCCompiler.__init__ (self, verbose, dry_run, force)
        compiler = self.cc_exe
                             linker_so=compiler + ' -shared')

Regarding the compiler flags I trusted the Intel people somewhat and took most of them from I read the docs for all of them, and they seem to make sense. But you should think these through for your environment. It is good to know what they mean.

Then build numpy:

python build --compiler=intelem | tee build.log

Copy the build to the Python tree (install):

python install | tee install.log

Validate that the numpy build works:

20:20:57 $ python
Python 2.7.6 (default, Feb 18 2014, 15:09:15) 
[GCC 4.1.2 20080704 (Red Hat 4.1.2-52)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import numpy
>>> numpy.__version__
>>> numpy.test()
Running unit tests for numpy
NumPy version 1.8.0
NumPy is installed in /projects/bioinfp_apps/Python-2.7.6/lib/python2.7/site-packages/numpy
Python version 2.7.6 (default, Feb 18 2014, 15:09:15) [GCC 4.1.2 20080704 (Red Hat 4.1.2-52)]
nose version 1.3.0
[... snip ...]
Ran 4969 tests in 63.975s
<nose.result.TextTestResult run=4969 errors=0 failures=0>

That looks great. Proceed.

Build, install, and validate scipy

Now that numpy is installed(!), we can go ahead with building scipy. Extract the scipy source, enter the source directory and invoke

python config --compiler=intelem --fcompiler=intelem build_clib --compiler=intelem --fcompiler=intelem build_ext --compiler=intelem --fcompiler=intelem install | tee build_install.log

See how we here enforce usage of intelem (which is just a label/name) for all components? This is important. The scipy build process uses the same build settings as used by numpy, especially the distutils compiler abstraction stuff (which is why numpy needs to installed before — this is a simple fact that the official docs do not explain well). I built and installed at the same time, on purpose. When doing build and install in separate steps, the install step involves some minor compilation tasks which are then performed using gfortran instead of ifort. At first I thought that this is none of an issue, but when executing scipy.test() I soon got a segmentation fault, due to mixing of compilers. When using the command as above (basically taken from, the test result is positive:

Python 2.7.6 (default, Feb 18 2014, 15:09:15) 
[GCC 4.1.2 20080704 (Red Hat 4.1.2-52)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import scipy
>>> scipy.test()
Running unit tests for scipy
NumPy version 1.8.0
NumPy is installed in /projects/bioinfp_apps/Python-2.7.6/lib/python2.7/site-packages/numpy
SciPy version 0.13.3
SciPy is installed in /projects/bioinfp_apps/Python-2.7.6/lib/python2.7/site-packages/scipy
Python version 2.7.6 (default, Feb 18 2014, 15:09:15) [GCC 4.1.2 20080704 (Red Hat 4.1.2-52)]
nose version 1.3.0
/projects/bioinfp_apps/Python-2.7.6/lib/python2.7/site-packages/numpy/lib/ DeprecationWarning: `scipy.lib.blas` is deprecated, use `scipy.linalg.blas` instead!
  warnings.warn(depdoc, DeprecationWarning)
/projects/bioinfp_apps/Python-2.7.6/lib/python2.7/site-packages/numpy/lib/ DeprecationWarning: `scipy.lib.lapack` is deprecated, use `scipy.linalg.lapack` instead!
  warnings.warn(depdoc, DeprecationWarning)
[ ... snip ...]
ERROR: test_fitpack.TestSplder.test_kink
Traceback (most recent call last):
  File "/projects/bioinfp_apps/Python-2.7.6/lib/python2.7/site-packages/nose/", line 197, in runTest
  File "/projects/bioinfp_apps/Python-2.7.6/lib/python2.7/site-packages/scipy/interpolate/tests/", line 329, in test_kink
    splder(spl2, 2)  # Should work
  File "/projects/bioinfp_apps/Python-2.7.6/lib/python2.7/site-packages/scipy/interpolate/", line 1186, in splder
    "and is not differentiable %d times") % n)
ValueError: The spline has internal repeated knots and is not differentiable 2 times
Ran 8934 tests in 131.506s
FAILED (KNOWNFAIL=115, SKIP=220, errors=1)
<nose.result.TextTestResult run=8934 errors=1 failures=0>

The one failing test is a corner case known to fail in some scenarios ( I might be able to narrow it down and help resolving named issue.

Congratulations, done. Enjoy the performance.