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 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 scipy-stack ambertools/21	StdEnv/2020 gcc/9.3.0 cuda/11.4 openmpi/4.0.3 scipy-stack ambertools/21	GCC, FlexiBLAS & 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
amber/16	StdEnv/2016.4 amber/16		Available only on Graham. Some Python functionality is not supported

Using modules

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).

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.

Job submission examples

Single GPU job

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.

File : pmemd_cuda.sh

#!/bin/bash
#SBATCH --cpus-per-task=1 
#SBATCH --gpus-per-node=1 
#SBATCH --mem-per-cpu=2000 
#SBATCH --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

CPU-only parallel MPI job

The example below requests four full nodes on Narval (64 tasks per node). If --nodes=4 is omitted SLURM will decide how many nodes to use based on availability.

File : pmemd_MPI.sh

#!/bin/bash
#SBATCH --nodes=4
#SBATCH --ntasks=512 
#SBATCH --mem-per-cpu=2000
#SBATCH --time=1:0:0
module purge
module load StdEnv/2020 gcc/9.3.0 cuda/11.4 openmpi/4.0.3 amber/20.12-20.15
srun pmemd.MPI -O -i input.in -p topol.parm7 -c coord.rst7 -o output.mdout -r restart.rst7

QM/MM distributed multi-GPU job

The example below requests eight GPUs.

File : pmemd_MPI.sh

#!/bin/bash
#SBATCH --ntasks=8 
#SBATCH --cpus-per-task=1 
#SBATCH --gpus-per-task=1
#SBATCH --mem-per-cpu=4000 
#SBATCH --time=1:00:00
module purge
module load StdEnv/2020 gcc/9.3.0 cuda/11.4 openmpi/4.0.3 ambertools/21 
source $EBROOTAMBERTOOLS/amber.sh 
srun sander.quick.cuda.MPI -O -i input.in -p topol.parm7 -c coord.rst7 -o output.mdout -r restart.rst7

Parallel MMPBSA job

The example below uses 32 MPI processes. MMPBSA scales linearly because each trajectory frame is processed independently.

File : pmemd_MPI.sh

#!/bin/bash
#SBATCH --ntasks=32 
#SBATCH --mem-per-cpu=4000 
#SBATCH --time=1:00:00
module purge
module load StdEnv/2020 gcc/9.3.0 openmpi/4.0.3 amber/20.9-20.15 scipy-stack
srun MMPBSA.py.MPI -O -i mmpbsa.in -o mmpbsa.dat -sp solvated_complex.parm7 -cp complex.parm7 -rp receptor.parm7 -lp ligand.parm7 -y trajectory.nc

You can modify scripts to fit your simulation requirements for computing resources. See Running jobs for more details.

Performance

For a quick estimate of the time and resources required for a simulation, visit our new Molecular Dynamics Performance Guide[1]. or follow direct links to AMBER benchmarks below.

View benchmarks of simulations with PMEMD[2]

View benchmarks of QM/MM simulations with SANDER.QUICK [3].

GNU Compiler Collection, an open source compiler collection

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

Message Passing Interface