Intel MKL: BLAS, LAPACK, ScaLAPACK

The Intel Maths Kernel Libraries (MKL) contain a variety of optimised numerical libraries including BLAS, LAPACK, and ScaLAPACK. In general, the exact commands required to build against MKL depend on the details of compiler, environment, requirements for parallelism, and so on. The Intel MKL link line advisor should be consulted.

See https://software.intel.com/content/www/us/en/develop/articles/intel-mkl-link-line-advisor.html

Some examples are given below. Note that loading the appropriate intel tools module will provide the environment variable MKLROOT which holds the location of the various MKL components.

Intel Compilers

BLAS and LAPACK

To use MKL libraries with the Intel compilers you just need to load the relevant Intel compiler module, and the Intel cmkl module, e.g.:

module load intel-20.4/fc
module load intel-20.4/cmkl

To include MKL you specify the -mkl option on your compile and link lines. For example, to compile a simple Fortran program with MKL you could use:

ifort -c -mkl -o lapack_prb.o lapack_prb.f90
ifort -mkl -o lapack_prb.x lapack_prb.o

The -mkl flag without any options builds against the threaded version of MKL. If you wish to build against the serial version of MKL, you would use -mkl=sequential.

ScaLAPACK

The distributed memory linear algebra routines in ScaLAPACK require MPI in addition to the compiler and MKL libraries. Here we use Intel MPI via:

module load intel-20.4/fc
module load intel-20.4/mpi
module load intel-20.4/cmkl

ScaLAPACK requires the Intel versions of BLACS at link time in addition to ScaLAPACK libraries; remember also to use the MPI versions of the compilers:

mpiifort -c -o linsolve.o linsolve.f90
mpiifort -o linsolve.x linsolve.o -L${MKLROOT}/lib/intel64 \
-lmkl_scalapack_lp64 -lmkl_intel_lp64 -lmkl_sequential -lmkl_core \
-lmkl_blacs_intelmpi_lp64 -lpthread -lm -ldl

GNU Compiler

BLAS and LAPACK

To use MKL libraries with the GNU compiler you first need to load the GNU compiler module and Intel MKL module, e.g.,:

module load gcc
module load intel-20.4/cmkl

To include MKL you need to link explicitly against the MKL libraries. For example, to compile a single source file Fortran program with MKL you could use:

gfortran -c -o lapack_prb.o lapack_prb.f90
gfortran -o lapack_prb.x lapack_prb.o -L$MKLROOT/lib/intel64 \
-lmkl_gf_lp64 -lmkl_core -lmkl_sequential

This will build against the serial version of MKL; to build against the threaded version use:

gfortran -c -o lapack_prb.o lapack_prb.f90
gfortran -fopenmp -o lapack_prb.x lapack_prb.o -L$MKLROOT/lib/intel64 \
-lmkl_gf_lp64 -lmkl_core -lmkl_gnu_thread

ScaLAPACK

The distributed memory linear algebra routines in ScaLAPACK require MPI in addition to the MKL libraries. On Cirrus, this is usually provided by SGI MPT.

module load gcc
module load mpt
module load intel-20.4/cmkl

Once you have the modules loaded you need to link against two additional libraries to include ScaLAPACK. Note we use here the relevant mkl_blacs_sgimpt_lp64 version of the BLACS library. Remember to use the MPI versions of the compilers:

mpif90 -f90=gfortran -c -o linsolve.o linsolve.f90
mpif90 -f90=gfortran -o linsolve.x linsolve.o -L${MKLROOT}/lib/intel64 \
-lmkl_scalapack_lp64 -lmkl_intel_lp64 -lmkl_sequential -lmkl_core \
-lmkl_blacs_sgimpt_lp64 -lpthread -lm -ldl

ILP vs LP interface layer

Many applications will use 32-bit (4-byte) integers. This means the MKL 32-bit integer interface should be selected (which gives the _lp64 extensions seen in the examples above).

For applications which require, e.g., very large array indices (greater than 2^31-1 elements), the 64-bit integer interface is required. This gives rise to _ilp64 appended to library names. This may also require -DMKL_ILP64 at the compilation stage. Check the Intel link line advisor for specific cases.