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    Compilation on CPU CLX

    Compilation on CPU CLX

    Okt. 18, 2024

    Content

    Code execution

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

    Code compilation

    Intel oneAPI compiler

    Serial code execution Quelle erweitern 
    module load intel
icx -o hello.bin hello.c
ifx -o hello.bin hello.f90
icpx -o hello.bin hello.cpp
    OpenMP threaded code execution Quelle erweitern 
    module load intel
icx -fopenmp -o hello.bin hello.c
ifx -fopenmp -o hello.bin hello.f90 
icpx -fopenmp -o hello.bin hello.cpp

    GNU compiler

    Serial code execution Quelle erweitern 
    module load gcc
gcc -o hello.bin hello.c
gfortran -o hello.bin hello.f90
g++ -o hello.bin hello.cpp
    OpenMP threaded code execution Quelle erweitern 
    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

    • 1 node,
    • 1 OpenMP code running.
    Serial Quelle erweitern 
    #SBATCH --nodes=1
#SBATCH --partition=cpu-clx:test
./hello.bin
    OpenMP, full node Quelle erweitern 
    #SBATCH --nodes=1
#SBATCH --partition=cpu-clx:test
export OMP_PROC_BIND=spread
export OMP_NUM_THREADS=96
./hello.bin
    OpenMP, half node Quelle erweitern 
    #SBATCH --nodes=1
#SBATCH --partition=cpu-clx:test
export OMP_PROC_BIND=spread
export OMP_NUM_THREADS=48
./hello.bin
    OpenMP, hyperthreading Quelle erweitern 
    #SBATCH --nodes=1
#SBATCH --partition=cpu-clx: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.
    OpenMP simultaneously Quelle erweitern 
    #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
    OpenMP simultaneously hyperthreading Quelle erweitern 
    #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

    Compiler flags

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

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

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

    With the GNU compilers, the corresponding compiler flags are

    -march=cascadelake -mprefer-vector-width=512

    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 Intel's link line advisor to see what libraries are recommended for a particular use case.

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