Content
Code execution
For examples for code execution, please visit Slurm partition CPU CLX.
Code compilation
Intel oneAPI compiler
| Codeblock |
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| title | Serial icccode execution |
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| collapse | true |
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module load intel
iccicx -o hello.bin hello.c
ifortifx -o hello.bin hello.f90
icpcicpx -o hello.bin hello.cpp |
| Codeblock |
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| title | OpenMP iccthreaded code execution |
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| collapse | true |
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module load intel
iccicx -qopenmpfopenmp -o hello.bin hello.c
ifortifx -qopenmpfopenmp -o hello.bin hello.f90
icpcicpx -qopenmpfopenmp -o hello.bin hello.cpp |
...
GNU compiler
| Codeblock |
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| title | Serial gcccode execution |
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| collapse | true |
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module load gcc
gcc -o hello.bin hello.c
gfortran -o hello.bin hello.f90
g++ -o hello.bin hello.cpp |
| Codeblock |
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| title | OpenMP gccthreaded code execution |
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| collapse | true |
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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 |
Code execution
To execute your code you need to
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Slurm job script
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The examples for slurm jobs scripts job scripts, e.g. myjobscipt.slurm, that cover the setup
- 1 node,
- 1 OpenMP code running.
| Codeblock |
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#SBATCH --nodes=1
#SBATCH --partition=cpu-clx:test
./hello.bin |
| Codeblock |
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| title | OpenMP, full node |
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| collapse | true |
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#SBATCH --nodes=1
#SBATCH --partition=standard96cpu-clx:test
export OMP_PROC_BIND=spread
export OMP_NUM_THREADS=96
./hello.bin |
...
| Codeblock |
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| title | OpenMP, half node |
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| collapse | true |
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#SBATCH --nodes=1
#SBATCH --partition=standard96cpu-clx:test
export OMP_PROC_BIND=spread
export OMP_NUM_THREADS=48
./hello.bin |
...
| Codeblock |
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| title | OpenMP, hyperthreading |
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| collapse | true |
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#SBATCH --nodes=1
#SBATCH --partition=standard96cpu-clx:test
export OMP_PROC_BIND=spread
export OMP_NUM_THREADS=192
./hello.bin |
...
| Codeblock |
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| title | OpenMP simultaneously |
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| collapse | true |
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#SBATCH --nodes=2
#SBATCH --partition=standard96cpu-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 |
...
| Codeblock |
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| title | OpenMP simultaneously hyperthreading |
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| collapse | true |
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#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, AVX512 AVX-512 instructions and the 512bit 512-bit ZMM registers can be used with the following compile flags with of the Intel compilers:
-xCORE-AVX512 -qopt-zmm-usage=high
However, high ZMM register usage is not recommended in all cases (read more
Image Removed).
With the GNU compilers (GCC 7.x and later), architecture-specific optimization for Skylake and Cascade Lake CPUs is enabled with, the corresponding compiler flags are
-march=skylake-avx512cascadelake -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 the link below Intel's link line advisor to see what libraries are recommended for a particular use case. https://software.intel.com/en-us/articles/intel-mkl-link-line-advisor/Image Removed