Content

Code execution

For examples for code execution, please visit Slurm CPU Genoa partition CPU CLX.

Code compilation

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Codeblock
Codeblock
title Serial icc collapse true
module load intel icc -o hello.bin hello.c ifort -o hello.bin hello.f90 icpc -o hello.bin hello.cpp

Codeblock
title OpenMP icc collapse true
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

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

Slurm job script

The examples for slurm job scripts, e.g. myjobscipt.slurm, that cover the setup

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#SBATCH --nodes=1
#SBATCH --partition=cpu-clx:testgenoa
./hello.bin

#SBATCH --nodes=1
#SBATCH --partition=cpu-clx:testgenoa
export OMP_PROC_BIND=spread
export OMP_NUM_THREADS=96
./hello.bin

#SBATCH --nodes=1
#SBATCH --partition=cpu-clx:test
export OMP_PROC_BIND=spread
export OMP_NUM_THREADS=48192
./hello.bin

#SBATCH --nodes=1
#SBATCH --partition=cpu-clx:testgenoa
export OMP_PROC_BIND=spread
export OMP_NUM_THREADS=192384
./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=cpu-clx: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 moreImage Removed).

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.

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