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. You can use either mpirun or srun as the job starter for CP2K. If you opt for mpirun, then, apart from loading the corresponding impi or openmpi modules, CPU and/or GPU pinning should be carefully carried out.
CP2K Version | Modulefile | Requirement | Compute Partitions | Support | CPU/GPU |
|---|---|---|---|---|---|
7.1 |
|
| CPU CLX | omp libint fftw3 libxc elpa parallel mpi3 scalapack xsmm spglib mkl | / |
2022.2 |
|
| CentOS 7 | libint, fftw3, libxc, elpa, scalapack, cosma, xsmm, spglib, mkl, sirius, libvori and libbqb | / |
2023.1 |
|
| CentOS 7 | Lise: libint, fftw3, libxc, elpa, scalapack, cosma, xsmm, spglib, mkl, sirius, libvori and libbqb. Emmy: libint, fftw3, libxc, elpa, scalapack, cosma, xsmm, spglib, mkl and sirius. | / |
2023.1 |
|
| GPU A100 | libint, fftw3, libxc, elpa, elpa_nvidia_gpu, scalapack, cosma, xsmm, dbcsr_acc, spglib, mkl, sirius, offload_cuda, spla_gemm, m_offloading, libvdwxc | / |
2023.2 |
|
| CentOS 7 | libint, fftw3, libxc, elpa, scalapack, cosma, xsmm, spglib, mkl, sirius, libvori and libbqb | / |
2023.2 |
|
| GPU A100 | libint, fftw3, libxc, elpa, elpa_nvidia_gpu, scalapack, cosma, xsmm, dbcsr_acc, spglib, mkl, sirius, offload_cuda, spla_gemm, m_offloading, libvdwxc | / |
2024.1 |
|
| CPU CLX | omp,libint,fftw3,fftw3_mkl,libxc,elpa,parallel,mpi_f08,scalapack,xsmm,spglib,mkl,sirius,hdf5 | / |
2024.1 |
|
| CPU Genoa | omp,fftw3,libxc,elpa,parallel,mpi_f08,scalapack,cosma,xsmm,spglib,sirius,hdf5 | / |
2025.1 |
|
| CPU CLX | omp,libint,fftw3,fftw3_mkl,libxc,elpa,parallel,scalapack,mpi_f08,cosma,xsmm,spglib,mkl,libdftd4,sirius,hdf5 | / |
2025.1 |
|
| CPU Genoa | omp,libint,fftw3,libxc,elpa,parallel,mpi_f08,scalapack,cosma,xsmm,spglib,sirius,hdf5 | / |
2025.2 |
|
| CPU Genoa | omp, libint, fftw3, libxc, elpa, parallel, scalapack, mpi_f08, xsmm, plumed2, spglib, libdftd4 | / |
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), possibly needed by Fortran users, are in $CP2K_MOD_DIR.
For more details, please refer to the documentation.
Example Jobscripts
For compute nodes on CPU CLX
#!/bin/bash
#SBATCH --time=12:00:00
#SBATCH --partition=cpu-clx
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=24
#SBATCH --cpus-per-task=4
#SBATCH --job-name=cp2k
export SLURM_CPU_BIND=none
export OMP_NUM_THREADS=${SLURM_CPUS_PER_TASK}
# Binding OpenMP threads
export OMP_PLACES=cores
export OMP_PROC_BIND=close
# Binding MPI tasks
export I_MPI_PIN=yes
export I_MPI_PIN_DOMAIN=omp
export I_MPI_PIN_CELL=core
# Our tests have shown that CP2K has better performance with psm2 as libfabric provider
# Check if this also apply to your system
# To stick to the default provider, comment out the following line
export FI_PROVIDER=psm2
# Select the appropriate Intel-MPI module
module load impi/2021.14
# Select the appropriate version
module load cp2k/2025.1
mpirun cp2k.psmp input > outputFor compute nodes on CPU Genoa
#!/bin/bash
#SBATCH --time=12:00:00
#SBATCH --partition=cpu-genoa
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=48
#SBATCH --cpus-per-task=4
#SBATCH --job-name=cp2k
export SLURM_CPU_BIND=none
export OMP_NUM_THREADS=${SLURM_CPUS_PER_TASK}
# Binding OpenMP threads
export OMP_PLACES=cores
export OMP_PROC_BIND=close
module load openmpi/gcc/5.0.3
# Select the appropriate version
module load cp2k/2024.1
# Do not use srun combined with export SLURM_CPU_BIND=none
# Important: here we are using mpirun to start the MPI process. The pinning is performed according to the following line
mpirun --bind-to core --map-by ppr:${SLURM_NTASKS_PER_NODE}:node:pe=${OMP_NUM_THREADS} cp2k.psmp input > outputFor Nvidia A100 GPU nodes
#!/bin/bash
#SBATCH --partition=gpu-a100
#SBATCH --time=12:00:00
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=4
#SBATCH --cpus-per-task=18
#SBATCH --job-name=cp2k
export SLURM_CPU_BIND=none
export OMP_NUM_THREADS=${SLURM_CPUS_PER_TASK}
export OMP_PLACES=cores
export OMP_PROC_BIND=close
module load gcc/11.3.0 openmpi/gcc.11/4.1.4 cuda/11.8 cp2k/2023.2
# gpu_bind.sh (see the following script) should be placed inside the same directory where cp2k will be executed
# Don't forget to make gpu_bind.sh executable by running: chmod +x gpu_bind.sh
mpirun --bind-to core --map-by numa:PE=${SLURM_CPUS_PER_TASK} ./gpu_bind.sh cp2k.psmp input > outputgpu_bind.sh
#!/bin/bash
export CUDA_VISIBLE_DEVICES=$OMPI_COMM_WORLD_LOCAL_RANK
$@Remark on OpenMP
Depending on the problem size, the code may terminate with a segmentation fault due to an insufficient stack size or because threads exceed their allocated stack space. To circumvent this, we recommend inserting the following in the job script:
export OMP_STACKSIZE=512M
ulimit -s unlimited