LAMMPS¶

LAMMPS is a large scale classical molecular dynamics code, and stands for Large-scale Atomic/Molecular Massively Parallel Simulator. LAMMPS has potentials for soft materials (biomolecules, polymers), solid-state materials (metals, semiconductors) and coarse-grained or mesoscopic systems. It can be used to model atoms or, more generically, as a parallel particle simulator at the atomic, meso, or continuum scale.

Availability and Supported Architectures¶

LAMMPS is available at NERSC as a provided support level package. LAMMPS runs at NERSC are currently supported on CPU and GPU nodes.

Application Information, Documentation and Support¶

The official LAMMPS is available at LAMMPS Online Manual. LAMMPS has a large user base and good user support. Question related to using LAMMPS can be posted to the LAMMPS User forum. The forum also contains an archive of all past mailing list messages, which can be useful to help resolve some of the common user issues.

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Using LAMMPS at NERSC¶

To use the default version of LAMMPS, type:

module load lammps

Using LAMMPS on Perlmutter¶

LAMMPS can be run on both, CPU and GPU nodes of Perlmutter. The following are the two example scripts that can be used to submit a batch job to either of the nodes.

#!/bin/bash
#SBATCH -C cpu
#SBATCH -t 00:20:00
#SBATCH -J LAMMPS_CPU
#SBATCH -o LAMMPS_CPU.o%j
#SBATCH -A mXXXX
#SBATCH -N 1
#SBATCH --ntasks-per-node=128
#SBATCH -q regular

module load gsl
module load cray-hdf5-parallel
module load cray-fftw
module load lammps

exe=lmp
input="<please change this section to what is needed to run your simulation>"

command="srun -n 128 -c 2 --cpu_bind=cores lmp $input"

echo $command

$command 

The above script launches a 1 CPU node job running with 128 tasks (equal to the number of cores available on 1 CPU node).

#!/bin/bash -l
#SBATCH -C gpu
#SBATCH -t 00:20:00
#SBATCH -J LAMMPS_GPU
#SBATCH -o LAMMPS_GPU.o%j
#SBATCH -A mXXXX
#SBATCH -N 1
#SBATCH -c 32
#SBATCH --ntasks-per-node=4
#SBATCH --gpus-per-task=1
#SBATCH --gpu-bind=none
#SBATCH -q regular

module load gsl
module load cray-hdf5-parallel
module load cray-fftw
module load lammps

exe=lmp
input="-k on g 4 -sf kk -pk kokkos newton on neigh half -in in.snap.test -var nsteps 20 -var nx 10 -var ny 10 -var nz 80 -var snapdir 2J14_InflatedFrom_2J10/"

command="srun -n 4 --cpu_bind=cores lmp $input"

echo $command

$command 

Please change the project number to number assigned to your project where it says mXXXX. The example above uses 1 GPU node on PM, which has 4 GPUs each. When changing the number of nodes, please modify the line SBATCH -N 1 to whatever number of nodes you want to run your problem with. Additionally, please change the line 'command="srun -n 4 lmp $input"' to -n as number of nodes times 4. Please change line 'input=' in accordance to LAMMPS GPU use guide as suitable for your problem.

If running LAMMPS with Kokkos package, please review the Kokkos package page to add appropriate flags to the job submission line as well as modify the input script.

Building LAMMPS from source¶

Some users may be interested in building LAMMPS from source to enable more specific LAMMPS packages. The source files for LAMMPS can be downloaded as either a tar file or from the LAMMPS Github repository.

module load cudatoolkit
module load craype-accel-nvidia80

git clone https://github.com/lammps/lammps.git
cd lammps
mkdir build
cd build

cmake -DCMAKE_INSTALL_PREFIX=$PWD/../install_pm -D CMAKE_BUILD_TYPE=Release \
            -D CMAKE_Fortran_COMPILER=ftn -D CMAKE_C_COMPILER=cc -D CMAKE_CXX_COMPILER=CC \
            -D MPI_C_COMPILER=cc -D MPI_CXX_COMPILER=CC -D LAMMPS_EXCEPTIONS=ON \
            -D BUILD_SHARED_LIBS=ON -D PKG_KOKKOS=yes -D Kokkos_ARCH_AMPERE80=ON -D Kokkos_ENABLE_CUDA=yes \
            -D PKG_MANYBODY=ON -D PKG_MOLECULE=ON -D PKG_KSPACE=ON -D PKG_REPLICA=ON -D PKG_ASPHERE=ON \
            -D PKG_RIGID=ON -D PKG_MPIIO=ON \
            -D CMAKE_POSITION_INDEPENDENT_CODE=ON -D CMAKE_EXE_FLAGS="-dynamic" ../cmake
make -j16
make install

Related Applications¶

• NAMD

User Contributed Information¶