Alexandre Savio
Published

Tue 08 April 2014

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Compile Numpy and Scipy against Intel MKL

Tested on Ubuntu 13.10 and 14.04

1. Install C++ Composer and FORTRAN Composer, with MKL

Fill forms and download online installation shell scripts from here.

OpenBLAS Note

If you want to use OpenBLAS as well, install it and follow the OpenBLAS notes along this tutorial. I haven’t tested this yet.

But first, install it:

git clone git://github.com/xianyi/OpenBLAS
cd OpenBLAS && make FC=gfortran
sudo make PREFIX=/opt/OpenBLAS install
sudo ldconfig

2. Execute shell scripts with sudo

I will assume you’ll install the libraries in /opt/intel.

3. Once installed, add this to ~/.bashrc

#Intel C++ Studio
if [ -d /opt/intel ];
then
    export INTEL_HOME=/opt/intel
    export PATH=${PATH}:${INTEL_HOME}/bin
    export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/opt/intel/lib/intel64:${LD_LIBRARY_PATH}
fi

4. Add this to /etc/ld.so.conf.d/intel.conf:

/opt/intel/lib/intel64
/opt/intel/lib/intel64/irml
/opt/intel/lib/intel64/crt
/opt/intel/mkl/lib/intel64

5. Download Numpy and Scipy from here

6. Change Numpy source code:

Change directory to numpy-x.x.x

Create a site.cfg from the existing site.cfg.examle

Edit site.cfg as follows:

Add the following lines to site.cfg in your top level NumPy directory to use IntelĀ® MKL, if you are building on Intel 64 platform, assuming the default path for the Intel MKL installation from the Intel Parallel Studio XE 2013 or Intel Composer XE 2013 versions:

[mkl]
library_dirs = /opt/intel/mkl/lib/intel64
include_dirs = /opt/intel/mkl/include
mkl_libs = mkl_rt
lapack_libs =

If you are building NumPy for 32 bit, please add as the following:

[mkl]
library_dirs = /opt/intel/mkl/lib/ia32
include_dirs = /opt/intel/mkl/include
mkl_libs = mkl_rt
lapack_libs =

Change cc_exe in numpy/distutils/intelccompiler.py to be something like:

self.cc_exe = \'icc -O3 -g -fPIC -fp-model strict -fomit-frame-pointer -openmp -xhost\'

Here we use, -O3, optimizations for speed and enables more aggressive loop transformations such as Fusion, Block-Unroll-and-Jam, and collapsing IF statements, -openmp for OpenMP threading and -xhost option tells the compiler to generate instructions for the highest instruction set available on the compilation host processor. If you are using the ILP64 interface, please add -DMKL_ILP64 compiler flag.

Run icc --help for more information on processor-specific options, and refer Intel Compiler documentation for more details on the compiler flags.

Change the Fortran compiler flags in numpy-x.x.x/numpy/distutil/fcompiler/intel.py to use the following compiler options for the Intel Fortran Compiler:

For ia32 and Intel64

ifort -xhost -openmp -fp-model strict -fPIC

Change the get_flags_opt function line to:

return ['-xhost -openmp -fp-model strict']

If you are using ILP64 interface of Intel MKL, please add -i8 flag above. If you are using older versions of Numpy/SciPy, please refer the new intel.py for your reference from the latest version of NumPy, which you can replace to use the above mentioned compiler options.

OpenBLAS Note

To use OpenBLAS, uncomment the following lines and correct the paths:

[openblas]
libraries = openblas
library_dirs = /opt/OpenBLAS/lib
include_dirs = /opt/OpenBLAS/include

7. Check your config

cd <numpy-x.x.x>
python setup.py config

You should see the MKL library paths, and OpenBLAS if you enabled it.

8. Compile Numpy and Scipy with the following command (once for Numpy and then once for Scipy):

Remember to activate the virtual environment if you are going to use this in one.

8.1 For 64-bit:

cd <numpy-x.x.x>
python setup.py config --compiler=intelem --fcompiler=intelem build_clib \
--compiler=intelem --fcompiler=intelem build_ext --compiler=intelem --fcompiler=intelem install

cd <scipy-x.x.x>
python setup.py config --compiler=intelem --fcompiler=intelem build_clib \
--compiler=intelem --fcompiler=intelem build_ext --compiler=intelem --fcompiler=intelem install

8.2 For 32-bit:

cd <numpy-x.x.x>
python setup.py config --compiler=intel --fcompiler=intel build_clib \
--compiler=intel --fcompiler=intel build_ext --compiler=intel --fcompiler=intel install

cd <scipy-x.x.x>
python setup.py config --compiler=intel --fcompiler=intel build_clib \
--compiler=intel --fcompiler=intel build_ext --compiler=intel --fcompiler=intel install

9. Troubleshooting:

9.1 Compiling Scipy: “Using deprecated NumPy API, disable it by…”

Thi may be because of the version of GCC, try using 4.7 (worked in November 2013):

sudo apt-get install gcc-4.7
sudo rm /usr/bin/gcc
sudo ln -s /usr/bin/gcc-4.7 /usr/bin/gcc

Compile both Numpy and Scipy again.

10. Testing

You can test OpenBLAS with the following code (got from https://gist.github.com/osdf/):

#!/usr/bin/env python
import numpy
import sys
import timeit

try:
    import numpy.core._dotblas
    print 'FAST BLAS'
except ImportError:
    print 'slow blas'

print "version:", numpy.__version__
print "maxint:", sys.maxint
print

x = numpy.random.random((1000,1000))

setup = "import numpy; x = numpy.random.random((1000,1000))"
count = 5

t = timeit.Timer("numpy.dot(x, x.T)", setup=setup)
print "dot:", t.timeit(count)/count, "sec"

References

  1. https://software.intel.com/en-us/articles/numpyscipy-with-intel-mkl

  2. http://gehrcke.de/2014/02/building-numpy-and-scipy-with-intel-compilers-and-intel-mkl-on-a-64-bit-machine/

  3. http://stackoverflow.com/questions/11443302/compiling-numpy-with-openblas-integration

  4. https://gist.githubusercontent.com/osdf/3842524/raw/df01f7fa9d849bec353d6ab03eae0c1ee68f1538/test_numpy.py

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