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LAMMPS

LAMMPS, is a classical molecular dynamics code, and an acronym for Large-scale Atomic/Molecular Massively Parallel Simulator. LAMMPS has potentials for solid-state materials (metals, semiconductors) and soft matter (biomolecules, polymers) 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.

Using LAMMPS on Cirrus

LAMMPS is Open Source software, and is freely available to all Cirrus users. A number of versions are available:

  • Parallel MPI: using intel-20.4 mpi
  • MPI+GPU version: using intel-20.4 mpi and CUDA11.8

To see what versions are currently available: 

  $ module avail lammps
The current default central module is based on the 15Dec2023 release.

Running parallel LAMMPS jobs (MPI)

LAMMPS can exploit multiple nodes on Cirrus and will generally be run in exclusive mode over more than one node.

For example, the following script will run a LAMMPS MD job using 4 nodes (144 cores) with pure MPI.

#!/bin/bash --login

# Slurm job options (name, compute nodes, job time)
#SBATCH --job-name=lammps_Example
#SBATCH --time=00:20:00
#SBATCH --exclusive
#SBATCH --nodes=4
#SBATCH --tasks-per-node=36
#SBATCH --cpus-per-task=1

# Replace [budget code] below with your project code (e.g. t01)
#SBATCH --account=[budget code]
# Replace [partition name] below with your partition name (e.g. standard,gpu)
#SBATCH --partition=[partition name]
# Replace [qos name] below with your qos name (e.g. standard,long,gpu)
#SBATCH --qos=[qos name]

# Load LAMMPS module
module load lammps

# Run using input in in.test
srun lmp < in.test

Running parallel LAMMPS jobs (GPU)

LAMMPS can exploit multiple GPUs, although the performance scaling depends heavily on the particular system, so each user should run benchmarks for their particular use-case. While not every LAMMPS forcefield/fix are available for GPU, a vast majority is, and more are added with each new version. Check the LAMMPS documentation for GPU compatibility with a specific command.

For example, the following script will run a LAMMPS MD job using 2 GPUs, 1 MPI thread per GPU:

#!/bin/bash --login

# Slurm job options (name, compute nodes, job time)
#SBATCH --job-name=lammps_Example
#SBATCH --time=00:20:00
#SBATCH --nodes=1
#SBATCH --gres=gpu:2

# Replace [budget code] below with your project code (e.g. t01)
#SBATCH --account=[budget code]
# Replace [partition name] below with your partition name (e.g. standard,gpu)
#SBATCH --partition=[partition name]
# Replace [qos name] below with your qos name (e.g. standard,long,gpu)
#SBATCH --qos=[qos name]

# Load LAMMPS module
module load lammps-gpu

# Run using input in in.test
srun lmp -sf gpu -pk gpu 2 -in input.file -l log.file

to increase the number of cores-per-gpu used, either use OpenMP or increase the number of MPI threads, using the --ntasks and --ntasks-per-node SBATCH flags:

#!/bin/bash --login

# Slurm job options (name, compute nodes, job time)
#SBATCH --job-name=lammps_Example
#SBATCH --time=00:20:00
#SBATCH --nodes=1
#SBATCH --gres=gpu:2

# Replace [budget code] below with your project code (e.g. t01)
#SBATCH --account=[budget code]
# Replace [partition name] below with your partition name (e.g. standard,gpu)
#SBATCH --partition=[partition name]
# Replace [qos name] below with your qos name (e.g. standard,long,gpu)
#SBATCH --qos=[qos name]

# Load LAMMPS module
module load lammps-gpu

# --ntasks and --ntasks-per-node should be up to 10 x number of gpus requested.
export PARAMS=" --ntasks-per-node=20 --ntasks=20 --exclusive"
# Run using input in in.test
srun ${PARAMS} lmp -sf gpu -pk gpu 2 -in in.ethanol_optimized

Compiling LAMMPS on Cirrus

Compile instructions for LAMMPS on Cirrus can be found on GitHub: