Molecular dynamics with GPU containers

Overview

Teaching: 5 min
Exercises: 10 min

Questions

Objectives

• Get started with Nvidia GPU containers for HPC applications

Note

To run exercises from this episode on your own, you’ll need a machine with a GPU card and GPU drivers installed.
There are examples for both using and not using the Slurm scheduler.

ERNZ20 attendees only: let’s login to a GPU node

We’ll be skipping this one today sorry, but please read through the example and we’ll update this to work on NeSI in future.

Run a molecular dynamics simulation on a GPU with containers

For our example we are going to use Gromacs, a quite popular molecular dynamics package, among the ones that have been optimised to run on GPUs through Nvidia containers.

First, let us cd into demos/05_gromacs, and ensure that $SIFPATH is defined:

$ cd $ERNZ20/demos/05_gromacs
$ export SIFPATH=$ERNZ20/demos/sif

Now, let’s verify that the container image nvcr.io/hpc/gromacs:2018.2 has been correctly downloaded:

$ ls $SIFPATH/gromacs*

/home/ubuntu/ernz20-containers/demos/sif/gromacs_2018.2.sif

The current directory has got sample input files picked from the collection of Gromacs benchmark examples. In particular, we’re going to use the subset water-cut1.0_GMX50_bare/1536/. First let’s gunzip one of the required input files:

$ gunzip conf.gro.gz

Now, from a Singularity perspective, all we need to do to run a GPU application from a container is to add the runtime flag --nv. This will make Singularity look for the Nvidia drivers in the host, and mount them inside the container.

On the host system side, when running GPU applications through Singularity the only requirement consists of the Nvidia driver for the relevant GPU card (the corresponding file is typically called libcuda.so.<VERSION> and is located in some library subdirectory of /usr).

Do not execute the next two commands, let us just have a look at them.

• Preliminary step

  $ singularity exec --nv $SIFPATH/gromacs_2018.2.sif gmx grompp -f pme.mdp
  

• Production step

  $ singularity exec --nv $SIFPATH/gromacs_2018.2.sif gmx mdrun -ntmpi 1 -nb gpu -pin on -v -noconfout -nsteps 5000 -s topol.tpr -ntomp 1
  

GPU resources are usually made available in HPC systems through schedulers, to which Singularity natively and transparently interfaces. So, for instance let us have a look in the current directory at the Slurm batch script called gpu.sh:

#!/bin/bash -l

#SBATCH --job-name=gpu
#SBATCH --gres=gpu:1
#SBATCH --ntasks=1
#SBATCH --time=01:00:00

# run Gromacs preliminary step with container
srun singularity exec --nv $SIFPATH/gromacs_2018.2.sif \
    gmx grompp -f pme.mdp

# Run Gromacs MD with container
srun singularity exec --nv $SIFPATH/gromacs_2018.2.sif \
    gmx mdrun -ntmpi 1 -nb gpu -pin on -v -noconfout -nsteps 5000 -s topol.tpr -ntomp 1

Basically, we have just combined the Slurm command srun with singularity exec <..>. We can submit the script with:

$ sbatch gpu.sh

Running this example at Pawsey

If you try and run this on Zeus at Pawsey, you might want to add module load singularity after the #SBATCH lines in the script. You might also want to edit the submission command as follows:

$ sbatch --account=<your-pawsey-project> --partition=gpuq gpu.sh

Nvidia GPU Cloud, a useful resource

The GPU manufacturer Nvidia has a dedicated web registry for container images, shipping GPU optimised applications: https://ngc.nvidia.com.

To browse this registry, you’ll need a free account. Go to https://ngc.nvidia.com, complete the procedure to Create an account, then Sign in (currently both >options are available on the top right corner of the page).

You can browse the available containerised packages through the various category boxes. E.g. click on the High Performance Computing box, then click on the >Gromacs one.

Key Points

• You can run containerised GPU applications using the flag --nv

• Singularity transparently interfaces with HPC schedulers such as Slurm