intel-mkl.md

Intel MKL 
=========

  

Intel Math Kernel Library
-------------------------

Intel Math Kernel Library (Intel MKL) is a library of math kernel
subroutines, extensively threaded and optimized for maximum performance.
Intel MKL provides these basic math kernels:

-   

    

    BLAS (level 1, 2, and 3) and LAPACK linear algebra routines,
    offering vector, vector-matrix, and matrix-matrix operations.
-   

    

    The PARDISO direct sparse solver, an iterative sparse solver,
    and supporting sparse BLAS (level 1, 2, and 3) routines for solving
    sparse systems of equations.
-   

    

    ScaLAPACK distributed processing linear algebra routines for
    Linux* and Windows* operating systems, as well as the Basic Linear
    Algebra Communications Subprograms (BLACS) and the Parallel Basic
    Linear Algebra Subprograms (PBLAS).
-   

    

    Fast Fourier transform (FFT) functions in one, two, or three
    dimensions with support for mixed radices (not limited to sizes that
    are powers of 2), as well as distributed versions of
    these functions.
-   

    

    Vector Math Library (VML) routines for optimized mathematical
    operations on vectors.
-   

    

    Vector Statistical Library (VSL) routines, which offer
    high-performance vectorized random number generators (RNG) for
    several probability distributions, convolution and correlation
    routines, and summary statistics functions.
-   

    

    Data Fitting Library, which provides capabilities for
    spline-based approximation of functions, derivatives and integrals
    of functions, and search.
-   Extended Eigensolver, a shared memory  version of an eigensolver
    based on the Feast Eigenvalue Solver.

For details see the [Intel MKL Reference
Manual](http://software.intel.com/sites/products/documentation/doclib/mkl_sa/11/mklman/index.htm).

Intel MKL version 13.5.192 is available on Anselm

    $ module load mkl

The module sets up environment variables, required for linking and
running mkl enabled applications. The most important variables are the
$MKLROOT, $MKL_INC_DIR, $MKL_LIB_DIR and $MKL_EXAMPLES

The MKL library may be linked using any compiler.
With intel compiler use -mkl option to link default threaded MKL.

### Interfaces

The MKL library provides number of interfaces. The fundamental once are
the LP64 and ILP64. The Intel MKL ILP64 libraries use the 64-bit integer
type (necessary for indexing large arrays, with more than 231^-1
elements), whereas the LP64 libraries index arrays with the 32-bit
integer type.

    |Interface|Integer type|
  -----  |---|---|-------------------------------------
    |LP64|32-bit, int, integer(kind=4), MPI_INT|
  ILP64       64-bit, long int, integer(kind=8), MPI_INT64

### Linking

Linking MKL libraries may be complex. Intel [mkl link line
advisor](http://software.intel.com/en-us/articles/intel-mkl-link-line-advisor)
helps. See also [examples](intel-mkl.html#examples) below.

You will need the mkl module loaded to run the mkl enabled executable.
This may be avoided, by compiling library search paths into the
executable. Include  rpath on the compile line:

    $ icc .... -Wl,-rpath=$LIBRARY_PATH ...

### Threading

Advantage in using the MKL library is that it brings threaded
parallelization to applications that are otherwise not parallel.

For this to work, the application must link the threaded MKL library
(default). Number and behaviour of MKL threads may be controlled via the
OpenMP environment variables, such as OMP_NUM_THREADS and
KMP_AFFINITY. MKL_NUM_THREADS takes precedence over OMP_NUM_THREADS

    $ export OMP_NUM_THREADS=16
    $ export KMP_AFFINITY=granularity=fine,compact,1,0

The application will run with 16 threads with affinity optimized for
fine grain parallelization.

Examples
------------

Number of examples, demonstrating use of the MKL library and its linking
is available on Anselm, in the $MKL_EXAMPLES directory. In the
examples below, we demonstrate linking MKL to Intel and GNU compiled
program for multi-threaded matrix multiplication.

### Working with examples

    $ module load intel
    $ module load mkl
    $ cp -a $MKL_EXAMPLES/cblas /tmp/
    $ cd /tmp/cblas

    $ make sointel64 function=cblas_dgemm

In this example, we compile, link and run the cblas_dgemm  example,
demonstrating use of MKL example suite installed on Anselm.

### Example: MKL and Intel compiler

    $ module load intel
    $ module load mkl
    $ cp -a $MKL_EXAMPLES/cblas /tmp/
    $ cd /tmp/cblas
    $ 
    $ icc -w source/cblas_dgemmx.c source/common_func.c -mkl -o cblas_dgemmx.x
    $ ./cblas_dgemmx.x data/cblas_dgemmx.d

In this example, we compile, link and run the cblas_dgemm  example,
demonstrating use of MKL with icc -mkl option. Using the -mkl option is
equivalent to:

    $ icc -w source/cblas_dgemmx.c source/common_func.c -o cblas_dgemmx.x 
    -I$MKL_INC_DIR -L$MKL_LIB_DIR -lmkl_intel_lp64 -lmkl_intel_thread -lmkl_core -liomp5

In this example, we compile and link the cblas_dgemm  example, using
LP64 interface to threaded MKL and Intel OMP threads implementation.

### Example: MKL and GNU compiler

    $ module load gcc
    $ module load mkl
    $ cp -a $MKL_EXAMPLES/cblas /tmp/
    $ cd /tmp/cblas
     
    $ gcc -w source/cblas_dgemmx.c source/common_func.c -o cblas_dgemmx.x 
    -lmkl_intel_lp64 -lmkl_gnu_thread -lmkl_core -lgomp -lm

    $ ./cblas_dgemmx.x data/cblas_dgemmx.d

In this example, we compile, link and run the cblas_dgemm  example,
using LP64 interface to threaded MKL and gnu OMP threads implementation.

MKL and MIC accelerators
------------------------

The MKL is capable to automatically offload the computations o the MIC
accelerator. See section [Intel Xeon
Phi](../intel-xeon-phi.html) for details.

Further reading
---------------

Read more on [Intel
website](http://software.intel.com/en-us/intel-mkl), in
particular the [MKL users
guide](https://software.intel.com/en-us/intel-mkl/documentation/linux).