AMBER

Introduction

Amber is the collective name for a suite of programs that allow users to perform molecular dynamics simulations, particularly on biomolecules. None of the individual programs carry this name, but the various parts work reasonably well together, and provide a powerful framework for many common calculations.

Amber vs. AmberTools

We have modules for both Amber and AmberTools available in our software stack.

• The AmberTools (module ambertools) contain a number of tools for preparing and analysing simulations, as well as sander to perform molecular dynamics simulations, all of which are free and open source.
• Amber (module amber) contains everything that is included in ambertools, but adds the advanced pmemd program for molecular dynamics simulations.

To see a list of installed versions and which other modules they depend on, you can use the module spider command or check the Available software page.

Loading Amber and AmberTools modules

StdEnv/2020StdEnv/2016

AMBER version	modules for running on CPUs	modules for running on GPUs (CUDA)	Notes
ambertools/21	StdEnv/2020 gcc/9.3.0 openmpi/4.0.3 ambertools/21	StdEnv/2020 gcc/9.3.0 cuda/11.4 openmpi/4.0.3 ambertools/21	GCC, FlexiBLAS & FFTW
ambertools/21		StdEnv/2020 gcc/9.3.0 cuda/11.0 openmpi/4.0.3 ambertools/21	GCC, OpenBLAS & FFTW
amber/20.12-20.15	StdEnv/2020 gcc/9.3.0 openmpi/4.0.3 amber/20.12-20.15	StdEnv/2020 gcc/9.3.0 cuda/11.4 openmpi/4.0.3 amber/20.12-20.15	GCC, FlexiBLAS & FFTW
amber/20.9-20.15	StdEnv/2020 gcc/9.3.0 openmpi/4.0.3 amber/20.9-20.15	StdEnv/2020 gcc/9.3.0 cuda/11.0 openmpi/4.0.3 amber/20.9-20.15	GCC, MKL & FFTW
amber/18.14-18.17	StdEnv/2020 gcc/9.3.0 openmpi/4.0.3 amber/18.14-18.17	StdEnv/2020 gcc/8.4.0 cuda/10.2 openmpi/4.0.3	GCC, MKL

AMBER version	modules for running on CPUs	modules for running on GPUs (CUDA)	Notes
amber/18	StdEnv/2016 gcc/5.4.0 openmpi/2.1.1 scipy-stack/2019a amber/18	StdEnv/2016 gcc/5.4.0 openmpi/2.1.1 cuda/9.0.176 scipy-stack/2019a amber/18	GCC, MKL
amber/18.10-18.11	StdEnv/2016 gcc/5.4.0 openmpi/2.1.1 scipy-stack/2019a amber/18.10-18.11	StdEnv/2016 gcc/5.4.0 openmpi/2.1.1 cuda/9.0.176 scipy-stack/2019a amber/18.10-18.11	GCC, MKL
amber/18.10-18.11	StdEnv/2016 gcc/7.3.0 openmpi/3.1.2 scipy-stack/2019a amber/18.10-18.11	StdEnv/2016 gcc/7.3.0 cuda/9.2.148 openmpi/3.1.2 scipy-stack/2019a amber/18.10-18.11	GCC, MKL

Using Amber

AmberTools 21

Currently, AmberTools 21 module is available on all clusters. AmberTools provide the following MD engines: sander, sander.LES, sander.LES.MPI, sander.MPI, sander.OMP, sander.quick.cuda, and sander.quick.cuda.MPI. After loading the module set AMBER environment variables:

source $EBROOTAMBERTOOLS/amber.sh

Amber 20

There are two versions of amber/20 modules: 20.9-20.15 and 20.12-20.15. The first one uses MKL and cuda/11.0, while the second uses FlexiBLAS and cuda/11.4. MKL libraries do not perform well on AMD CPU, and FlexiBLAS solves this problem. It detects CPU type and uses libraries optimized for the hardware. CUDA/11.4 is required for running simulations on A100 GPUs installed on Narval.

CPU-only modules provide all MD programs available in AmberTools/20 plus pmemd (serial) and pmemd.MPI (parallel). GPU modules add pmemd.cuda (single GPU), and pmemd.cuda.MPI (multi - GPU).

Running GPU-accelerated PMEMD on Narval

For GPU-accelerated simulations on Narval, use amber/20.12-20.15. Modules compiled with cuda version < 11.4 do not work on A100 GPUs. Below is an example submission script for a single-GPU job with amber/20.12-20.15:

#!/bin/bash
#SBATCH --cpus-per-task=1 --gpus-per-node=1 --mem-per-cpu=2000 --time=10:0:0  
module purge
module load StdEnv/2020  gcc/9.3.0 cuda/11.4 openmpi/4.0.3 amber/20.12-20.15
pmemd.cuda -O -i input.in -p topol.parm7 -c coord.rst7 -o output.mdout -r restart.rst7

Known issues

1. Module amber/20.12-20.15 does not have MMPBSA.py.MPI executable.

2. MMPBSA.py from amber/18-10-18.11 and amber/18.14-18.17 modules can not perform PB calculations. Use more recent amber/20 modules for this type of calculations.

Older Amber versions

Amber 16 is currently available on Graham only due to license restrictions. It was built with the previous system environment StdEnv/2016.4. Load StdEnv/2016.4 before loading amber/16 using the module command:

[name@server $] module load StdEnv/2016.4
[name@server $] module load amber/16 

This version does not support some Python functionality of Amber.

Job submission

For a general discussion about submitting jobs, see Running jobs.

In examples below, change the module load command to the one shown above if you wish to use the newer version.

The following example is a sander serial job script. The input files are in.md, crd.md.23, prmtop.

File : amber_serial.sh

#!/bin/bash
 #SBATCH --ntasks=1             # 1 cpu, serial job
 #SBATCH --mem-per-cpu=2G       # memory per cpu
 #SBATCH --time=00-01:00        # time (DD-HH:MM)
 #SBATCH --output=cytosine.log  # .log file from scheduler
 module load StdEnv/2016.4
 module load amber/16
 sander -O  -i in.md  -c crd.md.23  -o cytosine.out

The following example is a sander.MPI parallel job script:

File : amber_parallel.sh

#!/bin/bash
 #SBATCH --nodes=1 --ntasks-per-node=32  # 1 node with 32 cpus, MPI job
 #SBATCH --mem-per-cpu=2G                # memory, should be less than 4G
 #SBATCH --time=00-01:00                 # time (DD-HH:MM)
 #SBATCH --output=sodium.log             # output .log file
 module load StdEnv/2016.4
 module load amber/16
 srun sander.MPI -ng 2 -groupfile groups

You can modify the script to fit your job's requirements for compute resources. See Running jobs.

GNU Compiler Collection, an open source compiler collection

Intel Math Kernel Library, a software library of optimized math routines

Message Passing Interface