AMBER

On HPC2N we have AMBER available as a module. To see which versions are available use:

module spider amber

Read the page about modules, to see how to load the required module.

Note: that Amber needs both C and Fortran libraries, so despite what it says on the output from 'module spider', you need to load both the icc and ifort modules it lists.

If you need to use xleap, or otherwise run applications which opens a display, you must login with

ssh -X

Note: There is rarely any benefit from running multi-gpu jobs on the NVIDIA Volta cards. Here is a quote from the Amber developers:

"With the latest Volta lines it's not possible to get better performance than the single GPU case unless you have some really specialized interconnects and
like eight of them--a corner case that's not worth replicating. Just stick to single GPU runs. The multi-GPU stuff remains in the code because we
will not break functionality that was added years ago, and we may return to the multi-GPU world in time, but for now the benefit of running multiple
GPUs on the Pascal architectures is marginal and Volta is nonexistent."

Running a job

Prepare the job on the login node (nab, xleap, ...) but do NOT run anything longer/heavier there. You should submit a job script to run sander and pmemd.

There are several tutorials at http://ambermd.org/tutorials/, where you can see how to prepare and run a job with AMBER.

Then, use a batch script similar to one of these, when you submit your job:

The following examples uses "LOAD-THE-MODULE" as a placeholder for the respective module load commands, which can be found with

ml spider amber

For starting MPI enabled programs one should use "srun".

First example

Uses sander.MPI, 8 tasks (cores). It references a groupfile and is derived from the Amber Tutorial A7, found here.

#!/bin/bash
#SBATCH -A <Your-Project-Here>
#SBATCH -n 8
#SBATCH --time=01:00:00

LOAD-THE-MODULE

srun sander.MPI -ng 8 -groupfile equilibrate.groupfile

Second example

Uses pmemd.MPI, 96 tasks (cores), 48 per node. It's derived from the Amber Tutorial 17, found here.

#!/bin/bash
#SBATCH -A <Your-Project-Here>
#SBATCH -n 96
#SBATCH --ntasks-per-node=48
#SBATCH --time=01:00:00

LOAD-THE_MODULE

srun pmemd.MPI -O -i 02_heat.in -o 02_heat.out -p ala_tri.prmtop -c 01_min.rst -r 02_heat.rst -x 02_heat.nc

It's useful to note that a groupfile simply specifies file input and output details and is used for convenience. Both sander.MPI and pmemd.MPI can be used with or without a groupfile. If a groupfile is not used, then proper input and output files need to be specified, like in the second example.

Note: that Amber prefers the number of cores to be a power of 2, i.e., 2, 4, 8, 16, 32, 64, etc.

Third example, using a single V100 card on Kebnekaise

#!/bin/bash
#SBATCH -J Amber
#SBATCH -A <Your-Project-Here>
#SBATCH -n 1
#SBATCH --gres=gpu:v100:1
#SBATCH --time=1:00:00

LOAD-THE-MODULE

pmemd.cuda -O -i mdinfile -o mdoutfile -c inpcrdfile -p prmtopfile -r restrtfile

The job is submitted with

sbatch <submitscript.sh>

A comparison of runs on the various types of nodes on Kebnekaise is displayed below. We evaluated the performance of different AMBER implementations including Sander-MPI (with 28 cores), PMEMD-MPI (with  28 cores), and PMEMD-GPU (with 1 MPI processes and 1 GPU card). The figure below shows the best performance of AMBER. The benchmark case consisted of 158944 particles, using 1 fs. for time step and a cutoff of 1.2 nm. for real space electrostatics calculations. Particle mesh Ewald was used to solve long-range electrostatic interactions. As a comparison, we used the same test case for Gromacs (see Gromacs page).