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	Lise/Emmy
2022.2	`cp2k/2022.2`	`intel/2021.2 (Lise)` `intel/2022.2 (Emmy)`	CentOS 7	libint, fftw3, libxc, elpa, scalapack, cosma, xsmm, spglib, mkl, sirius, libvori and libbqb	/	/
2023.1	`cp2k/2023.1`	`intel/2021.2 (Lise)` `intel/2022.2 (Emmy)`	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	`cp2k/2023.1`	`openmpi/gcc.11/4.1.4 cuda/11.8`	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	`cp2k/2023.2`	`intel/2021.2` `impi/2021.7.1`	CentOS 7	libint, fftw3, libxc, elpa, scalapack, cosma, xsmm, spglib, mkl, sirius, libvori and libbqb	/	/
2023.2	`cp2k/2023.2`	`openmpi/gcc.11/4.1.4 cuda/11.8`	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	`cp2k/2023.2`	`impi/2021.13`	Rocky Linux 9	omp,libint,fftw3,fftw3_mkl,libxc,elpa,parallel,mpi_f08,scalapack,xsmm,spglib,mkl,sirius,hdf5	/	/

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.

Example Jobscripts

Lise (using srun)

#!/bin/bash 
#SBATCH --time=12:00:00
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=24
#SBATCH --cpus-per-task=4
#SBATCH --job-name=cp2k

export OMP_NUM_THREADS=${SLURM_CPUS_PER_TASK}

module load intel/2021.2 impi/2021.7.1 cp2k/2023.2
srun cp2k.psmp input > output

Lise (using mpirun)

#!/bin/bash 
#SBATCH --time=12:00:00
#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

module load intel/2021.2 impi/2021.7.1 cp2k/2023.2
mpirun cp2k.psmp input > output

Lise (using mpirun): on 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 > output

gpu_bind.sh

#!/bin/bash
export CUDA_VISIBLE_DEVICES=$OMPI_COMM_WORLD_LOCAL_RANK
$@

Emmy (using srun)

#!/bin/bash
#SBATCH --time=12:00:00
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=24
#SBATCH --cpus-per-task=4
#SBATCH --job-name=cp2k

export OMP_NUM_THREADS=${SLURM_CPUS_PER_TASK}

module load intel/2022.2 impi/2021.6 cp2k/2023.1
srun cp2k.psmp input > output

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