1. Build a custom library: To interface with Intel MKL from Python we recommend you use the custom library builder in the tools/builder sub-directory of the Intel MKL package. The Intel® MKL User's Guide has documentation on this tool (docs online). Here briefly are the steps I took to do this:
   
   
   1. Set up your environment to use the desired version of Intel MKL:
      source /<MKLpath>/tools/environment/mklvarsem64t.sh
   2. Build the DLL:
      cd /<MKLpath>/tools/builder
make em64t name=~/libmkl4py export=cblas_list
   3. The library as built above will depend on the OpenMP* threading runtime library used by Intel MKL (libiomp5.so). You should make sure that both libraries, libmkl4py.so and libiomp5.so, are in a directory specified in the LD_LIBRARY_PATH environment variable.
2. Call Intel MKL in your Python script: The following is a simple script (also available here) that loads the shared library just created and calls the matrix function.
   

   from ctypes import *
   
   # Load the share library
   mkl = cdll.LoadLibrary("./libmkl4py.so")
   cblas_dgemm = mkl.cblas_dgemm
   
   def print_mat(mat, m, n):
     for i in xrange(0,m):
       print " ",
       for j in xrange(0,n):
         print mat[i*n+j],
       print 
   
   # Initialize scalar data
   Order = 101  # 101 for row-major, 102 for column major data structures
   TransA = 111 # 111 for no transpose, 112 for transpose, and 113 for conjugate transpose
   TransB = 111
   m = 2
   n = 4
   k = 3
   lda = k
   ldb = n
   ldc = n
   alpha = 1.0
   beta = -1.0
   
   # Create contiguous space for the double precision array
   amat = c_double * 6      
   bmat = c_double * 12
   cmat = c_double * 8
   
   # Initialize the data arrays
   a = amat(1,2,3, 4,5,6)
   b = bmat(0,1,0,1, 1,0,0,1, 1,0,1,0)
   c = cmat(5,1,3,3, 11,4,6,9)
   
   print "\nMatrix A ="
   print_mat(a,2,3) 
   print "\nMatrix B ="
   print_mat(b,3,4)
   print "\nMatrix C ="
   print_mat(c,2,4)
   
   print "\nCompute", alpha, "* A * B + ", beta, "* C"
   
   # Call Intel MKL by casting scalar parameters and passing arrays by reference
   cblas_dgemm( c_int(Order), c_int(TransA), c_int(TransB), \
                c_int(m), c_int(n), c_int(k), c_double(alpha), byref(a), c_int(lda), \
                byref(b), c_int(ldb), c_double(beta), byref(c), c_int(ldc))
   
   print_mat(c,2,4)
   print

3. A few notes:
   • Matrices in the BLAS and LAPACK parts of Intel MKL are stored in one dimensional arrays and integers are used to specify their geometry.
   • I've actually loaded here CBLAS interface to the general matrix multiply function which allows you to choose how the matrix is specified. In my script I've listed the matrix by rows (row-major ordering). If you do not use the cblas interface to the BLAS or if you use LAPACK you should keep in mind that these functions assume the Fortran method of listing matrices by columns (column-major ordering).

We extended the list of examples demonstrate how possible to call different ( not only widespread example like dgemm ) from the Python program:

See the list of 3 different examples attached:
dft.zip  - shows the Python program calls 1D DFTI API
spblas.zip - shows how to call  matrix-matrix multiplication routine for a sparse matrix stored in the block compressed format (BSR)
vsl.zip - shows how to call vdRngGaussian routine ( generates normally distributed random numbers)  from VSL domain.

Notes:
Each zip file contains  *.res and *_list files mean input file and file for custom library building correspondingly

Link: LAPACK examples

Introduction 

Several users have asked how to call and link the Intel® Math Kernel Library (Intel® MKL) functions from their C# programs. While the standard way of interfacing with third party software libraries from C# is well documented, some of the steps in interfacing with Intel MKL specifically may still be confusing. The attached source code package is intended to show how to navigate the whole process for Intel MKL users. These examples show how to create a DLL from Intel MKL libraries, call those functions from their C# source, and interface with that DLL.

Examples are provided for calling the BLAS, LAPACK, DFTI (the FFT interface), the PARDISO direct sparse solver, and the vector math library (VML).

Building the Examples

Follow these steps to build the example programs:

• unzip the contents of the attached zip file
• Open a Microsoft Visual Studio command prompt or add the Microsoft.NET Framework to the PATH environment variable in another command prompt
• Run the build script (makefile) using nmake
  • Example: nmake ia32 MKLROOT="c:\program files\intel\mkl\10.1.1.015"
  • The makefile provides further explanation of the parameters in comments

This will create an executable for each of the example programs.

Example files: Intel_MKL_C#_Examples.zip

Take our C++/C# interface survey for Intel® Math Kernel Library