Workflow compiler

Code compilation

Intel compiler

module load intel
icc -o hello.bin hello.c
ifort -o hello.bin hello.f90
icpc -o hello.bin hello.cpp

module load intel
icc -qopenmp -o hello.bin hello.c
ifort -qopenmp -o hello.bin hello.f90
icpc -qopenmp -o hello.bin hello.cpp

Gnu compiler

module load gcc
gcc -o hello.bin hello.c
gfortran -o hello.bin hello.f90
g++ -o hello.bin hello.cpp

module load gcc
gcc -fopenmp -o hello.bin hello.c
gfortran -fopenmp -o hello.bin hello.f90
g++ -fopenmp -o hello.bin hello.cpp

Code execution

You can run a single OpenMP code. The examples cover the setup

1 node,
1 OpenMP code running.

#SBATCH --nodes=1
./hello.bin

#SBATCH --nodes=1
#SBATCH --partition=standard96:test
export OMP_PROC_BIND=spread
export OMP_NUM_THREADS=96
./hello.bin

#SBATCH --nodes=1
#SBATCH --partition=standard96:test
export OMP_PROC_BIND=spread
export OMP_NUM_THREADS=48
./hello.bin

#SBATCH --nodes=1
#SBATCH --partition=standard96:test
export OMP_PROC_BIND=spread
export OMP_NUM_THREADS=192
./hello.bin

You can run different OpenMP codes at the same time. The examples cover the setup

2 nodes,
4 OpenMP codes run simultaneously.
The code is not MPI parallel. mpirun is used to start the codes only.

#SBATCH --nodes=2
#SBATCH --partition=standard96:test
module load impi/2019.5
export SLURM_CPU_BIND=none
export OMP_PROC_BIND=spread
export OMP_NUM_THREADS=48
mpirun -ppn 2 \
       -np 1 ./code1.bin : -np 1 ./code2.bin : -np 1 ./code3.bin : -np 1 ./code4.bin

#SBATCH --nodes=2
#SBATCH --partition=standard96:test
module load impi/2019.5
export SLURM_CPU_BIND=none
export OMP_PROC_BIND=spread
export OMP_NUM_THREADS=96
mpirun -ppn 2 \
       -np 1 ./code1.bin : -np 1 ./code2.bin : -np 1 ./code3.bin : -np 1 ./code4.bin

Intel compiler flags

To make full use of the vectorizing capabilities of the CPUs, AVX512 instructions and the 512bit ZMM registers can be used with the following compile flags with the Intel compilers:

-xCORE-AVX512 -qopt-zmm-usage=high

However, high ZMM usage is not recommended in all cases (read more).

With GNU compilers (GCC 7.x and later), architecture-specific optimization for Skylake and Cascade Lake CPUs is enabled with

-march=skylake-avx512

Using the Intel MKL

The Intel® Math Kernel Library (Intel® MKL) is designed to run on multiple processors and operating systems. It is also compatible with several compilers and third party libraries, and provides different interfaces to the functionality. To support these different environments, tools, and interfaces Intel MKL provides multiple libraries from which to choose.

Check out the link below to see what libraries are recommended for a particular use case. https://software.intel.com/en-us/articles/intel-mkl-link-line-advisor/