GROMACS

#!/bin/bash -l
#
# allocate 4 nodes with 20 cores per node = 4*20 = 80 MPI tasks
#SBATCH --nodes=4
#SBATCH --tasks-per-node=20
#
# allocate nodes for 6 hours
#SBATCH --time=06:00:00
# job name 
#SBATCH --job-name=my-gmx
# do not export environment variables
#SBATCH --export=NONE
#
# first non-empty non-comment line ends SBATCH options

# do not export environment variables
unset SLURM_EXPORT_ENV
# jobs always start in submit directory

module load gromacs/2021.5-gcc11.2.0-mkl

### 1) The argument of -maxh should match the requested walltime! 
### 2) Performance often can be optimized if -npme # with a proper number of pme tasks is specified; 
###    experiment of use tune_mpe to find the optimal value. 
###    Using the SMT threads can sometimes be beneficial, however, requires testing.
### 3) Number of openMP threads also has to be tested beforehand and is limited by the number of pme tasks.
srun gmx_mpi mdrun [-npme #] [-ntomp #] -maxh 6 -dlb yes -s my.tpr

#!/bin/bash -l
#SBATCH --job-name=my-gmx
#SBATCH --nodes=3
#SBATCH --ntasks-per-node=72
#SBATCH --partition=multinode
#SBATCH --cpus-per-task=1
#SBATCH --time=10:00:00
#SBATCH --export=NONE

unset SLURM_EXPORT_ENV

module load gromacs/2021.5-gcc11.2.0-impi-mkl

srun gmx_mpi mdrun [-npme #] -maxh 9.5 [-ntomp #] -dlb yes -s my.tpr

#!/bin/bash -l
# allocate nodes for 6 hours 
#SBATCH --time=06:00:00 
# job name 
#SBATCH --job-name=Testjob 
#SBATCH --gres=gpu:1
# do not export environment variables 
#SBATCH --export=NONE 

# do not export environment variables 
unset SLURM_EXPORT_ENV 

module load gromacs/2021.1-gcc-mkl-cuda11.2

### 1) the argument of -maxh should match the requested walltime!
### 2) optional arguments are: -pme gpu
###                            -bonded gpu
###                            -update gpu
gmx mdrun -maxh 6 -s my.tpr -nb gpu -pin on -pinstride 1 -ntmpi 1 -ntomp 8

### try automatic restart (adapt the conditions to fit your needs)
if [ -f confout.gro ]; then
   echo "*** confout.gro found; no re-submit required"
   exit
if [ $SECONDS -lt 1800 ]; then
   echo "*** no automatic restart as runtime of the present job was too short"
   exit
fi

sbatch.tinyx job_script

#!/bin/bash -l
#SBATCH --job-name=my-gmx
#SBATCH --time=24:00:00
#SBATCH --gres=gpu:a40:1
#SBATCH --export=NONE
unset SLURM_EXPORT_ENV

cd $SLURM_SUBMIT_DIR

module load gromacs/2021.5-gcc11.2.0-mkl-cuda

TPR=name-of-run-input-file

cp ./${TPR}.tpr $TMPDIR
cd $TMPDIR

gmx mdrun -s -ntmpi 1 -ntomp 16 -pme gpu -bonded gpu -update gpu -pin on -pinstride 1 -deffnm $TPR -cpi $SLURM_SUBMIT_DIR/$TPR

cp * $SLURM_SUBMIT_DIR
cd $SLURM_SUBMIT_DIR

The performance benefit of using multiple GPUs is often very low! You get much better throughout if you run multiple independent jobs on a single GPUs as shown above.

Even if using multiple GPUs do not use the MPI-parallel version (mdrun_mpi) but the thread-mpi version (gmx mdrun) of Gromacs. -ntmpi # usually should match the number of GPUs available.

#!/bin/bash -l 
# allocated one GPU 
#SBATCH --gres=gpu:gtx3080:2
# allocate nodes for 6 hours 
#SBATCH --time=06:00:00 
# job name #SBATCH --job-name=Testjob 
# do not export environment variables 
#SBATCH --export=NONE 

# do not export environment variables 
unset SLURM_EXPORT_ENV 

module load gromacs/2021.1-gcc-mkl-cuda11.2

### 1) The argument of -maxh should match the requested walltime!
### 2) Typical optional arguments are: -pme gpu
###                                    -bonded gpu

# these variables are needed for halo exchange and 
# optimized communication between the GPUs 
export GMX_GPU_DD_COMMS=true 
export GMX_GPU_PME_PP_COMMS=true 
export GMX_GPU_FORCE_UPDATE_DEFAULT_GPU=true
gmx mdrun -ntmpi 4 -ntomp 2 -maxh 6 -s my.tpr -npme 1 -pin on -pinstride 1

### try automatic restart (adapt the conditions to fit your needs)
if [ -f confout.gro ]; then
   echo "*** confout.gro found; no re-submit required"
   exit
if [ $SECONDS -lt 1800 ]; then
   echo "*** no automatic restart as runtime of the present job was too short"
   exit
fi

sbatch job_script

This is an example script for running a meta-dynamic simulation with 32 walkers with Gromacs patched with Plumed on eight of our RTX3080 GPUs. Transfer to other GPU hardware is possible, but may require adjustment of settings (e.g. MPS-server [y/n], flags for mpirun and Gromacs program flags).

Please note: The run-input-file (*.tpr) for each walker needs to be in its own directory and it must be given the same name inside that directory.

#!/bin/bash -l
# allocated one GPU 
#SBATCH --gres=gpu:gtx3080:8
# allocate nodes for 6 hours 
#SBATCH --time=06:00:00 
# job name 
#SBATCH --job-name=Testjob 
# do not export environment variables 
#SBATCH --export=NONE 

# do not export environment variables 
unset SLURM_EXPORT_ENV 

module load gromacs/2021.1-gcc-mkl-cuda11.2

TPR=name

# not necessary, but makes sure the directories are in correct order
directories=`echo dir{0..9} dir{1..2}{0..9} dir3{0..1}`

# these variables are needed to start the MPS-server
# Select a location that’s accessible to the given $UID
export CUDA_MPS_PIPE_DIRECTORY=/tmp/nvidia-mps.$SLURM_JOB_ID
export CUDA_MPS_LOG_DIRECTORY=/tmp/nvidia-log.$SLURM_JOB_ID
# Start the daemon.
nvidia-cuda-mps-control -d 

# these variables need to be placed directly before the Gromacs invocation
# these variables are needed for halo exchange and 
# optimized communication between the GPUs 
export GMX_GPU_DD_COMMS=true 
export GMX_GPU_PME_PP_COMMS=true 
export GMX_GPU_FORCE_UPDATE_DEFAULT_GPU=true

# --oversubscribe is necessary, otherwise mpirun aborts
# -s is needed, otherwise gromacs complains
# -pme -nb -update -bonded make sure everything is offloaded to the GPU
# -pin -pinstride order the threads on the CPU, otherwise there's 
#  wild chaos on the CPU
# -plumed ../plumed_in.dat needs to point to where the file is relative 
#  to the directory the .tpr is in

mpirun -np 32 --oversubscribe gmx_mpi mdrun -s $TPR -pme gpu -nb gpu -update gpu -bonded gpu -pin on -pinstride 1 -plumed ../plumed_in.dat -multidir ${directories} -cpi $TPR -maxh 6

# this will stop the MPS-server
echo quit | nvidia-cuda-mps-control