LAMMPS

LAMMPS stands for Large-scale Atomic/Molecular Massively Parallel Simulator and is copywrited by Sandia (DOE). See the On-line Manual for LAMMPS documentation, which is linked below. Here we give instructions how to run it on the CPUs and GPUs with the staff supported version on the Hazel HPC. Also given are instructions for users to install their own version.

External Links:
LAMMPS website
LAMMPS Tutorials
LAMMPS benchmarks


Typically, it's not a good idea to run lammps from your /home directory, as its output can easily overflow your quota.

The current staff-supported version is 22Jul2025 (stable_22Jul2025_update4), compiled with PrgEnv-intel/2025.3-slurm and cuda/12.0. The binary is named lmp_hazel, installed at /usr/local/apps/lammps/22Jul2025/intel/gpu/bin/lmp_hazel.
The modulefiles are:

module avail lammps/22Jul2025

They are:
lammps/22Jul2025/intel/gpu-slurm  (for GPU nodes)
lammps/22Jul2025/intel/cpu-slurm  (for CPU nodes)


Previous version (2024Aug29):
CPU only, no GPU support. This version predates the Slurm-integrated compiler stack.

Older versions (2022Jun23):
These are the previous GPU-capable staff-supported builds. Prior to the 22Jul2025 build, these were the only staff-supported versions with GPU acceleration.
Modulefiles:

• lammps/2022Jun23/intel/gpu  (recommended — Intel compiler, GPU nodes)
• lammps/2022Jun23/intel/cpu  (Intel compiler, CPU nodes)
• lammps/2022Jun23/gnu/gpu   (GNU compiler, GPU nodes)
• lammps/2022Jun23/gnu/cpu   (GNU compiler, CPU nodes)

Earlier versions (29Sep21u3, 7Aug19, 22Aug2018) are no longer supported.

Table of Contents - Select for a Particular Topic
• LAMMPS with GPUS
• LAMMPS with CPUS
• LAMMPS Hazel benchmarks
• How to install and maintain your own LAMMPS version.
• List of LAMMPS packages in the staff supported version.


• LAMMPS for GPUs

To test LAMMPS on the 22Jul2025 version for GPUs (interactively) do

salloc --ntasks=16 --nodes=1 --time=00:05:00 \
  --partition=gpu_partners --qos=short_gpu --gres=gpu:a30:2
module load lammps/22Jul2025/intel/gpu-slurm
srun lmp_hazel -sf gpu -in \
  /usr/local/apps/lammps/22Jul2025/intel/gpu/share/lammps/bench/in.lj

To test LAMMPS 22Jul2025 for GPUs in batch mode, create "lammps_gpu_benchmark_slurm.sh" with the following lines:

#!/bin/bash
#SBATCH --ntasks=16
#SBATCH --nodes=1
#SBATCH --time=00:05:00
#SBATCH --partition=gpu_partners
#SBATCH --qos=short_gpu
#SBATCH --gres=gpu:a30:2
#SBATCH --output=out.%j
#SBATCH --error=err.%j
module load lammps/22Jul2025/intel/gpu-slurm
srun lmp_hazel -sf gpu -in /usr/local/apps/lammps/22Jul2025/intel/gpu/share/lammps/bench/in.lj

and run it with:

sbatch lammps_gpu_benchmark_slurm.sh


Note: use --qos=short_gpu alongside --partition=gpu_partners for GPU jobs. Available GPU types can be checked with:

sinfo -o "%N %G %t" -p gpu_partners | grep idle


How to select a specific GPU:
• sinfo -o "%N %G %t" -p gpu_partners | grep idle
This shows the node name, GPU type/count, and state for idle nodes. Example output:


gpu[05-06] gpu:a30:2 idle
gpu13 gpu:a100:4 idle

Use the GPU type from the second column in your --gres flag. For example, to select the A30 GPU, use
--gres=gpu:a30:2
To use any of the older gpus (the RTX2080 or the GTX1080), use:
--gres=gpu:rtx2080:2 or --gres=gpu:gtx1080:2
To use the P100 GPU for applications that require double precision (eg. ab-initio applications), use:
--gres=gpu:p100:2
Most of the GPU nodes have 2 GPUs per node, but the rtx2080 has 4.

• To select a specific hostname, --nodelist (not recommended)
To select a specific hostname, use:
--nodelist=c041n01
• To avoid a specific hostname, --exclude (not recommended)
To avoid a specific hostname, use:
--exclude=c014n01


• LAMMPS for CPUs
To test LAMMPS version 22Jul2025 for CPUs (interactively) do

salloc --ntasks=16 --nodes=1 --time=00:10:00 \
  --partition=compute_partners --qos=short --constraint=cascadelake
module load lammps/22Jul2025/intel/cpu-slurm
srun lmp_hazel -in \
  /usr/local/apps/lammps/22Jul2025/intel/gpu/share/lammps/bench/in.lj

To do the same thing in batch mode, create "parallel_cpu_slurm.sh" with the following lines:

#!/bin/bash
#SBATCH --ntasks=16
#SBATCH --nodes=1
#SBATCH --time=00:10:00
#SBATCH --partition=compute_partners
#SBATCH --qos=short
#SBATCH --constraint=cascadelake
#SBATCH --output=out.%j
#SBATCH --error=err.%j
module load lammps/22Jul2025/intel/cpu-slurm
srun lmp_hazel -in /usr/local/apps/lammps/22Jul2025/intel/gpu/share/lammps/bench/in.lj

Run this batch job with:

sbatch parallel_cpu_slurm.sh

• LAMMPS Hazel benchmarks
Running the examples above on Hazel should give timings that meet or exceed the benchmarks shown on LAMMPS' website
Here is the link to the Hazel benchmarks.
lj model, cascadelake CPU node, 1 MPI rank (serial):
Performance: 1809.691 tau/day, 4.189 timesteps/s
lj model, cascadelake CPU node, 16 MPI ranks (parallel):
Performance: 26643.269 tau/day, 61.674 timesteps/s
lj model, A30 2x GPU node, 16 MPI ranks:
Performance: 141252.926 tau/day, 326.974 timesteps/s

• How to install and maintain your own LAMMPS version.

Users are encouraged to install and maintain their own version of LAMMPS in:

/usr/local/usrapps/projectid

Where "projectid" is a directory name given to the project by the HPC administrator (a request must be made). The following instructions describe how to compile LAMMPS from source using the stable branch. You can choose which packages to include at the CMake configuration step.

Step 1 — Get the source

git clone -b stable https://github.com/lammps/lammps.git src
cd src && git describe --tags

Step 2 — Load the build environment

module purge
module load PrgEnv-intel/2025.3-slurm
module load cuda/12.0
which mpiicx mpiicpx mpiifx nvcc g++   # all must resolve before continuing

Step 3 — Configure with CMake

Choose your packages with -D PKG_<NAME>=yes:

mkdir build && cd build
cmake \
  -D CMAKE_INSTALL_PREFIX=/usr/local/usrapps/[your_path]/lammps \
  -D CMAKE_BUILD_TYPE=Release \
  -D BUILD_MPI=yes -D BUILD_OMP=yes \
  -D CMAKE_C_COMPILER=$(which mpiicx) \
  -D CMAKE_CXX_COMPILER=$(which mpiicpx) \
  -D CMAKE_Fortran_COMPILER=$(which mpiifx) \
  -D CMAKE_CUDA_HOST_COMPILER=$(which g++) \
  -D CMAKE_CXX_FLAGS="-march=native" \
  -D CMAKE_C_FLAGS="-march=native" \
  -D LAMMPS_MACHINE=[your_machine_name] \
  -D FFT=MKL \
  -D PKG_GPU=yes -D GPU_API=cuda \
  -D GPU_ARCH="sm_61;sm_75;sm_80;sm_86;sm_89;sm_90" \
  -D GPU_PREC=mixed \
  -D PKG_INTEL=yes \
  -D PKG_OPENMP=yes \
  -D PKG_KSPACE=yes \
  -D PKG_MOLECULE=yes \
  -D PKG_MANYBODY=yes \
  -D PKG_RIGID=yes \
  # add or remove -D PKG_<NAME>=yes lines for any other packages you need
  # full list of available packages: https://docs.lammps.org/Packages_list.html
  ../cmake

After cmake runs, check the summary output to confirm your packages are listed under "Enabled packages" before building.

Step 4 — Build and install

make -j 8
make install

Note: Build in /tmp rather than your install directory to avoid hitting inode quotas on /usr/local. Delete the build tree after installation with rm -rf /tmp/your-build-dir.

The following alternative installation methods (conda and makefile) are preserved for reference and will be re-validated in a future update.

• List of packages that are installed in the staff supported version.

As of 22Jul2025 (stable_22Jul2025_update4), compiled with PrgEnv-intel/2025.3-slurm and cuda/12.0:
GPU, INTEL, KSPACE, MANYBODY, MOLECULE, OPENMP, RIGID

Last modified: July 01 2026 10:44:32.