NAMD is a parallel molecular dynamics code designed for the simulation of biomolecular systems. You should build an MPI version of the code from source instead of using one of the pre-compiled executables since these executables are not Slurm-aware. The directions for doing this depend on the cluster.

Container for GPU Clusters

We recommend using the NAMD 3 container from NVIDIA GPU Cloud. See our Singularity page for general directions on running containers.

CPU Clusters (e.g., Della)

These directions are for building an MPI verison of NAMD 2.13:

$ ssh <YourNetID>@della.princeton.edu  # or another CPU cluster
$ cd software  # or another directory
$ wget https://raw.githubusercontent.com/PrincetonUniversity/hpc_beginning_workshop/refs/heads/main/namd/namd_2.13_cpu_only.sh
$ bash namd_2.13_cpu_only.sh | tee build.log

This will build the namd2 executable in ~/software/NAMD_2.13_Source/Linux-x86_64-intel/. You may consider adding this path to your PATH environment variable.

Below is a sample Slurm script for a NAMD job on one of the CPU clusters:

#!/bin/bash
#SBATCH --job-name=namd-cpu      # create a short name for your job
#SBATCH --nodes=1                # node count
#SBATCH --ntasks=4               # total number of tasks across all nodes
#SBATCH --cpus-per-task=1        # cpu-cores per task (>1 if multi-threaded tasks)
#SBATCH --mem-per-cpu=4G         # memory per cpu-core (4G per cpu-core is default)
#SBATCH --time=00:10:00          # total run time limit (HH:MM:SS)
#SBATCH --mail-type=begin        # send email when job begins
#SBATCH --mail-type=end          # send email when job ends
#SBATCH --mail-type=fail         # send email if job fails
#SBATCH --mail-user=<YourNetID>@princeton.edu
module purge
module load intel-oneapi/2024.2 intel-mpi/oneapi/2021.13
srun </path/to/>namd2 <configfile>

Note that the build above produces a flat MPI version meaning it does not use threading. If you decide to use charmrun instead of srun then you will need to set -c 1 since the default is -c 2.

A benchmark system with 92K atoms can be obtained with:

$ wget https://www.ks.uiuc.edu/Research/namd/utilities/apoa1.tar.gz
$ tar zxf apoa1.tar.gz

GPU Clusters

NAMD can only use GPUs on a single node. Therefore it is fine to use the binary executable that they provide:

$ ssh <YourNetID>@tigergpu.princeton.edu
$ cd software  # or another directory
$ wget https://www.ks.uiuc.edu/Research/namd/2.13/download/412487/NAMD_2.13_Linux-x86_64-multicore-CUDA.tar.gz
$ tar zxf NAMD_2.13_Linux-x86_64-multicore-CUDA.tar.gz

Below is a Slurm script for running the single-node GPU version of NAMD:

#!/bin/bash
#SBATCH --job-name=namd-gpu      # create a short name for your job
#SBATCH --nodes=1                # node count
#SBATCH --ntasks-per-node=1      # total number of tasks across all nodes
#SBATCH --cpus-per-task=4        # cpu-cores per task (>1 if multi-threaded tasks)
#SBATCH --mem-per-cpu=4G         # memory per cpu-core (4G per cpu-core is default)
#SBATCH --time=00:10:00          # total run time limit (HH:MM:SS)
#SBATCH --gres=gpu:1             # number of gpus per node
#SBATCH --mail-type=begin        # send email when job begins
#SBATCH --mail-type=end          # send email when job ends
#SBATCH --mail-type=fail         # send email if job fails
#SBATCH --mail-user=<YourNetID>@princeton.edu
module purge
PATH=$PATH:</path/to>/NAMD_2.13_Linux-x86_64-multicore-CUDA
charmrun namd2 +p${SLURM_CPUS_PER_TASK} apoa1/apoa1.namd

Users should find the optimal value of --cpus-per-task for their work.

VMD

See this GitHub page for installing VMD on the clusters.