Forthcoming Intel® MKL 11.0 (Expected date of release: Autumn 2012)

•  Intel® MKL 11.0 will have backward incompatibility with Intel® MKL 10.2 update 3

•  Open MP static library on Microsoft* Windows

•  GMP* Arithmetic functions

•  Intel® MKL Reference Manual will be removed from product package

•  Intel® Pentium III will no longer be supported

•  Red Hat* EL4 and PGI* Fortran 10.x support will be dropped

•  PGI* Fortran 77 support will be removed

• a powerful N-dimensional array object
• sophisticated (broadcasting) functions
• tools for integrating C/C++ and Fortran code
• useful linear algebra, Fourier transform, and random number capabilities.

Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data.

For more information on NumPy, please visit http://NumPy.scipy.org/

Version Information

This application note was created to help NumPy users to make use of the latest versions of Intel MKL on Linux platforms.

The instructions given in this articles apply to Intel MKL 10.3 and above and Intel Compiler 11.0 and above.

 
Step 2 - Downloading NumPy Source Code

The NumPy source code can be downloaded from:

http://NumPy.scipy.org/

Prerequisites

Intel MKL can be obtained from the following options:

Download a FREE evaluation version of the Intel MKL product.
Download the FREE non-commercial* version of the Intel MKL product.

All of these can be obtained at: Intel® Math Kernel Library product web page.

Intel® MKL is also bundled with the following products

Intel® Parallel Studio XE 2011
Intel Parallel Composer XE 2011
Intel Cluster Studio 2011

Step 3 - Configuration

Use the following commands to extract the NumPy tar files from the downloaded NumPy-x.x.x.tar.gz.

1.	$gunzip numpy-x.x.x.tar.gz 
2.	$tar -xvf numpy-x.x.x.tar 

 
The above will create a directory named numpy-x.x.x

Make sure that C++ and FORTRAN compilers are installed and they are in PATH. Also set LD_LIBRARY_PATH to your compiler (C++ and FORTRAN), and MKL libraries.

Step 4 - Building NumPy

Change directory to numpy-x.x.x
Create a site.cfg from the existing one
 
Edit site.cfg as follows:

Add the following lines to site.cfg in your top level NumPy directory to use Intel® MKL, if you are building on Intel 64 platform:

[mkl]
library_dirs = /opt/intel/composer_xe_2011_sp1.6.233/mkl/lib/intel64
include_dirs = /opt/intel/composer_xe_2011.sp1.6.233/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/composer_xe_2011_sp1.6.233/mkl/lib/ia32
include_dirs = /opt/intel/composer_xe_2011_sp1.6.233/mkl/include
mkl_libs = mkl_rt
lapack_libs = 

Modify 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 various compiler flags.

Compile and install NumPy with the Intel compiler: (on 64-bit platforms replace "intel" with "intelem")

python setup.py config --compiler=intel build_clib --compiler=intel build_ext --compiler=intel install

If you build NumPY for Intel64 bit platforms:

$export LD_LIBRARY_PATH=/opt/intel/composer_xe_2011_sp1.6.233/mkl/lib/intel64:/opt/intel/composer_xe_2011_sp1.6.233/compiler/lib/intel64:$LD_LIBRARY_PATH

If you build NumPY for ia32 bit platforms:

$export LD_LIBRARY_PATH=/opt/intel/composer_xe_2011_sp1.6.233/mkl/lib/ia32:/opt/intel/composer_xe_2011_sp1.6.233/compiler/lib/ia32:$LD_LIBRARY_PATH

It is possible that LD_LIBRARY_PATH causes a problem, if you have installed MKL and Composer XE in other directories than the standard ones. The only solution I've found that always works is to build Python, NumPy and SciPy inside an environment where you've set the LD_RUN_PATH variable, e.g:

$export LD_RUN_PATH=~/opt/lib:~/intel/composer_xe_2011_sp1.6.233/compiler/lib:~/intel/composer_xe_2011_sp1.6.233/mkl/lib/ia32

Note: We recommend users to use arrays with 'C' ordering style which is row-major, which is default than Fortran Style which is column-major, and this is because NumPy uses CBLAS and also to get better performance.

Appendex A: Example:

Please see below an example Python script for matrix multiplication that you can use Numply installed with Intel MKL which has been provided for illustration purpose.

import numpy as np
import time

N = 6000
M = 10000

k_list = [64, 80, 96, 104, 112, 120, 128, 144, 160, 176, 192, 200, 208, 224, 240, 256, 384]

def get_gflops(M, N, K):
	return M*N*(2.0*K-1.0) / 1000**3

np.show_config()

for K in k_list:
	a = np.array(np.random.random((M, N)), dtype=np.double, order='C', copy=False)
	b = np.array(np.random.random((N, K)), dtype=np.double, order='C', copy=False)
	A = np.matrix(a, dtype=np.double, copy=False)
	B = np.matrix(b, dtype=np.double, copy=False)

	C = A*B

	start = time.time()

	C = A*B
	C = A*B
	C = A*B
	C = A*B
	C = A*B

	end = time.time()

	tm = (end-start) / 5.0

	print "{0:4}, {1:9.7}, {2:9.7}".format(K, tm, get_gflops(M, N, K) / tm)

Appendix B: Performance Comparison

Please click Examples.py to download the examples for LU, Cholesky and SVD.



 

• /Qstd=c99 on Windows
• -std=c99 on Linux and Mac OS X
• The default is C89 instead

The following C99 features are supported:

• restricted pointers (restrict keyword)
• variable-length Arrays
• flexible array members
• complex number support (_Complex keyword)
• hexadecimal floating-point constants
• compound literals
• designated initializers
• mixed declarations and code
• macros with a variable number of arguments
• inline functions (inline keyword)
• boolean type (_Bool keyword)

The following features are not supported: 

• long double (128-bit representations)

See C++0x Features Supported by Intel(R) C++ Compiler for supported C++0x features.