CP2K
Description
CP2K is a package for atomistic simulations of solid state, liquid, molecular, and biological systems offering a wide range of computational methods with the mixed Gaussian and plane waves approaches.
More information about CP2K and the documentation are found on https://www.cp2k.org/
Availability
CP2K is freely available for all users under the GNU General Public License (GPL).
Modules
CP2K is an MPI-parallel application. Use mpirun when launching CP2K.
The following versions are available via the unified GWDG Modules.
List of Modules
| Node Type | Module Names | Requirements (Load First) |
|---|---|---|
| Grete (GPU) | cp2k/2024.1 cp2k/2025.1 | gcc/13.2.0 openmpi/5.0.7 |
| Emmy (CPU) | cp2k/2023.2 cp2k/2024.1 cp2k/2025.1 | gcc/11.5.0 openmpi/4.1.7 |
| Emmy (CPU) | cp2k/2023.2 cp2k/2024.1 cp2k/2025.1 | gcc/14.2.0 openmpi/4.1.7 |
Remark: cp2k needs special attention when running on GPUs.
You need to check if, for your problem, a considerable acceleration is expected. E.g., for the following test cases, a performance degradation has been reported: https://www.cp2k.org/performance:piz-daint-h2o-64, https://www.cp2k.org/performance:piz-daint-h2o-64-ri-mp2, https://www.cp2k.org/performance:piz-daint-lih-hfx, https://www.cp2k.org/performance:piz-daint-fayalite-fist
GPU pinning is required (see the example of a job script below). Don’t forget to make executable the script that takes care of the GPU pinning. In the example, this is achieved with:
chmod +x gpu_bind.sh
Using cp2k as a library
Starting from version 2023.2, cp2k has been compiled enabling the option that allows it to be used as a library: libcp2k.a can be found inside $CP2K_LIB_DIR. The header libcp2k.h is located in $CP2K_HEADER_DIR, and the module files (.mod), eventually needed by Fortran users, are in $CP2K_MOD_DIR.
For more details, please refer to the documentation.
#!/bin/bash
#SBATCH --time=12:00:00
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=24
#SBATCH --cpus-per-task=4
#SBATCH --partition=standard96s
#SBATCH --job-name=cp2k
export OMP_NUM_THREADS=${SLURM_CPUS_PER_TASK}
module load gcc/14.2.0 openmpi/4.1.7 cp2k/2025.1
srun cp2k.psmp input > output#!/bin/bash
#SBATCH --partition=grete
#SBATCH --time=12:00:00
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=4
#SBATCH --cpus-per-task=16
#SBATCH --gpus-per-node=4
#SBATCH --job-name=cp2k
export SLURM_CPU_BIND=none
export OMP_NUM_THREADS=${SLURM_CPUS_PER_TASK}
export OMP_PLACES=cores
export OMP_PROC_BIND=close
module load gcc/13.2.0 openmpi/5.0.7 cp2k/2025.1
# gpu_bind.sh should be placed in the same directory where cp2k will be executed
# Don't forget to make it executable: chmod +x gpu_bind.sh
mpirun --bind-to core --map-by numa:PE=${SLURM_CPUS_PER_TASK} ./gpu_bind.sh cp2k.psmp input > output#!/bin/bash
export CUDA_VISIBLE_DEVICES=$OMPI_COMM_WORLD_LOCAL_RANK
$@Depending on the problem size, it may happen that the code stops with a segmentation fault due to insufficient stack size or due to threads exceeding their stack space. To circumvent this, we recommend inserting in the jobscript:
export OMP_STACKSIZE=512M
ulimit -s unlimited