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CP2K is freely available for all users under the GNU General Public License (GPL).

Modules

The CP2K module depends on intel/2021.2 (Lise) or on intel/2022.2 (Emmy).CP2K is an MPI-parallel application. You can use either mpirun or srun as the job starter for CP2K. If you use the opt for mpirun command, then you need to load , apart from loading the corresponding impi or openmpi modules before loading the CP2K module. Also , CPU and/or GPU pinning should be carefully carried out with mpirun.

CP2K Version	Modulefile	Requirement	Compute Partitions	Support	CPU/GPU	Lise/Emmy
7.1	cp2k/7.1	impi/2021.13	Rocky Linux 9	omp libint fftw3 libxc elpa parallel mpi3 scalapack xsmm spglib mkl	/	/
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.

1. 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

2. 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

#!/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 --time=06:00:00
#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

module load intelimpi/2021.2 impi/2021.7.113 

# Select the appropriate version
module load cp2k/20232024.1

srunmpirun cp2k.psmp input > output

#!/bin/bash 
#SBATCH --partition=gpu-a100  
#SBATCH --time =12:00:00
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=244
#SBATCH --cpus-per-task=418
#SBATCH --time=06:00:00
#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 intel/2022.2 impi/2021.6gcc/11.3.0 openmpi/gcc.11/4.1.4 cuda/11.8 cp2k/2023.1
srun cp2k.psmp h2o_f36.inp > output.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

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

Remark on OpenMP

Depending on the problem size, it may happen that the code stops may stop 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 jobscriptjob script:

export OMP_STACKSIZE=512M
ulimit -s unlimited

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