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Amber is a molecular dynamics package including a number of additional tools for more sophisticated analysis and in particular NMR structure refinement.


  • Puhti: 20, 20-cuda, 22, 22-cuda
  • Mahti: 20, 20-cuda, 22, 22-cuda
  • LUMI: 22-cpu, 22-gpu


Amber can be used on CSC servers by all not-for-profit institute and university researchers irrespective of nationality or location. Look for the academic license text here.


See available versions and how to load Amber by running:

module spider amber

The module load command will set $AMBERHOME and put the AmberTools binaries in the path. Run Amber production jobs in the batch queues, see below. Lightweight system preparation can be done on the login node as well (short serial AmberTools jobs).

Python modules

Please use the Amber22 modules on Puhti/Mahti if you intend to run the Python scripts distributed with AmberTools. These are not available in the older modules, nor on LUMI.

Molecular dynamics jobs are best run with pmemd.cuda. They are much faster on GPUs than on CPUs. Please note that using pmemd.cuda requires a module with the -cuda extension. Similarly, on LUMI one should use pmemd.hip (or pmemd.hip.MPI for multi-GPU simulations), which requires loading a module with the -gpu extension.


Run only GPU aware binaries in the GPU partitions. If you're unsure, check with seff <slurm_jobid> that GPUs were used and that the job was significantly faster than without GPUs.

Our tests show that for medium-sized systems the most efficient setup is one GPU card and one CPU core. An example batch script for Puhti would be:

#!/bin/bash -l
#SBATCH --time=00:10:00
#SBATCH --partition=gputest
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --account=<project>
#SBATCH --gres=gpu:v100:1

# 1 task, 1 thread, 1 GPU

module purge
module load gcc/9.4.0 openmpi/4.1.4
module load amber/22-cuda

srun pmemd.cuda -O -i mdin -r restrt -x mdcrd -o mdout


If you want to use more than one GPU, perform scaling tests to verify that the jobs really become faster and use a binary with .cuda.MPI or .hip.MPI extension. The rule of thumb is that when you double the resources, the job duration should decrease at least 1.5-fold. For overall performance info, consult the Amber benchmark scaling details. Typically, best efficiency is achieved with 1 GPU. For example, the Cellulose NPT benchmark does not scale well to multiple GPUs, but it is still massively faster on a single GPU than the CPU version (see diagram below).

You can find example inputs from the Amber20 tests directory:

ls $AMBERHOME/test

The non-GPU aware binaries, e.g. AmberTools, can be run as batch jobs in the following way (on Puhti):

#!/bin/bash -l
#SBATCH --time=00:10:00
#SBATCH --partition=test
#SBATCH --ntasks=1
#SBATCH --account=<project>

# 1 task

module purge
module load gcc/9.4.0 openmpi/4.1.4
module load amber/22

srun paramfit -i -p prmtop -c mdcrd -q QM_data.dat


pmemd.cuda and pmemd.hip are way faster than pmemd.MPI, so use a CPU-version only in case you cannot use the GPU-version. If Amber performance is not fast enough, consider using Gromacs, which can make use of more CPU cores (i.e. scales further). In particular, for large scale or very long MD simulations consider using a better scaling MD engine. An alternative is to run ensemble simulations using multi-pmemd.

Interactive jobs

Sometimes it is more convenient to run small jobs, like system preparations, interactively. To prevent excessive load on the login node, these kinds of jobs should be run as interactive batch jobs. You can request a shell on a compute node from the Puhti Web Interface, from the command line with sinteractive, or manually access to a single core with:

srun -n 1 -p test -t 00:05:00 --account=<project> --pty /bin/bash

Once you have been allocated resources (you might need to wait), you can run e.g. the paramfit task directly with:

paramfit -i -p prmtop -c mdcrd -q QM_data.dat

Amber on LUMI

Amber can be loaded into use on LUMI with:

module use /appl/local/csc/modulefiles
module load amber/22-gpu
# or
module load amber/22-cpu


You need to run the module use command to modify your $MODULEPATH, otherwise modules pre-installed by CSC cannot be accessed.

Example batch job script for LUMI-G:

#!/bin/bash -l
#SBATCH --partition=small-g
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --gpus-per-node=1
#SBATCH --time=01:00:00
#SBATCH --account=<project>

module purge
module use /appl/local/csc/modulefiles
module load amber/22-gpu

srun pmemd.hip.MPI -O -i mdin.GPU -o mdout.GPU -p Cellulose.prmtop -c Cellulose.inpcrd

A performance comparison of Amber on CPUs and GPUs on Puhti, Mahti and LUMI is shown in the bar plot below. Note how the performance of a single GPU on all systems is an order of magnitude better than a full Mahti CPU node (128 cores).

Amber scaling on GPUs and CPUs on Puhti, Mahti and LUMI

GPU binding on LUMI

For best performance, multi-GPU simulations on LUMI-G are likely to benefit from GPU binding. For background and instructions, see the LUMI documentation.

General batch script examples for LUMI-G and LUMI-C are available in the LUMI documentation.

High-throughput computing with Amber

Similar to Gromacs multidir, Amber has a built-in "multi-pmemd" functionality, which allows you to run multiple MD simulations within a single Slurm allocation. This is an efficient option in cases where you want to run many similar, but independent, simulations. Typical use cases are enhanced sampling methods such as umbrella sampling or replica exchange MD. Also, since Amber simulations do not scale that well to multiple GPUs, multi-pmemd can be used as a straightforward method to accelerate sampling by launching several differently initialized copies of your system, all running simultaneously on a single GPU each.


GPU resources on Puhti and Mahti are scarce, so we recommend running large-scale multi-pmemd simulations only on LUMI. LUMI-G has a massive GPU capacity available, which is also more affordable in terms of BUs compared to Puhti and Mahti.

An example multi-pmemd batch script for LUMI-G is provided below.

#!/bin/bash -l
#SBATCH --partition=standard-g
#SBATCH --nodes=2
#SBATCH --ntasks-per-node=8
#SBATCH --gpus-per-node=8
#SBATCH --time=01:00:00
#SBATCH --account=<project>

module purge
module use /appl/local/csc/modulefiles
module load amber/22-gpu

srun pmemd.hip.MPI -ng 16 -groupfile groupfile

In this example, 16 copies of a system are run concurrently within a single Amber job, each using 1 GPU. From the perspective of Slurm, each node on LUMI-G contains 8 GPUs, so 2 nodes are requested in total. The input, output, topology and coordinate files for the respective simulations are defined in a so-called groupfile:

-O -i mdin.GPU -o mdout000.GPU -p system000.prmtop -c system000.inpcrd
-O -i mdin.GPU -o mdout001.GPU -p system001.prmtop -c system001.inpcrd
-O -i mdin.GPU -o mdout002.GPU -p system002.prmtop -c system002.inpcrd
-O -i mdin.GPU -o mdout003.GPU -p system003.prmtop -c system003.inpcrd
-O -i mdin.GPU -o mdout004.GPU -p system004.prmtop -c system004.inpcrd
-O -i mdin.GPU -o mdout005.GPU -p system005.prmtop -c system005.inpcrd
-O -i mdin.GPU -o mdout006.GPU -p system006.prmtop -c system006.inpcrd
-O -i mdin.GPU -o mdout007.GPU -p system007.prmtop -c system007.inpcrd
-O -i mdin.GPU -o mdout008.GPU -p system008.prmtop -c system008.inpcrd
-O -i mdin.GPU -o mdout009.GPU -p system009.prmtop -c system009.inpcrd
-O -i mdin.GPU -o mdout010.GPU -p system010.prmtop -c system010.inpcrd
-O -i mdin.GPU -o mdout011.GPU -p system011.prmtop -c system011.inpcrd
-O -i mdin.GPU -o mdout012.GPU -p system012.prmtop -c system012.inpcrd
-O -i mdin.GPU -o mdout013.GPU -p system013.prmtop -c system013.inpcrd
-O -i mdin.GPU -o mdout014.GPU -p system014.prmtop -c system014.inpcrd
-O -i mdin.GPU -o mdout015.GPU -p system015.prmtop -c system015.inpcrd

See the Amber manual for further details on multi-pmemd.


When citing Amber22 or AmberTools22, please use the following:

D.A. Case, H.M. Aktulga, K. Belfon, I.Y. Ben-Shalom, J.T. Berryman, S.R. Brozell, D.S. Cerutti, T.E. Cheatham, III, G.A. Cisneros, V.W.D. Cruzeiro, T.A. Darden, R.E. Duke, G. Giambasu, M.K. Gilson, H. Gohlke, A.W. Goetz, R. Harris, S. Izadi, S.A. Izmailov, K. Kasavajhala, M.C. Kaymak, E. King, A. Kovalenko, T. Kurtzman, T.S. Lee, S. LeGrand, P. Li, C. Lin, J. Liu, T. Luchko, R. Luo, M. Machado, V. Man, M. Manathunga, K.M. Merz, Y. Miao, O. Mikhailovskii, G. Monard, H. Nguyen, K.A. O'Hearn, A. Onufriev, F. Pan, S. Pantano, R. Qi, A. Rahnamoun, D.R. Roe, A. Roitberg, C. Sagui, S. Schott-Verdugo, A. Shajan, J. Shen, C.L. Simmerling, N.R. Skrynnikov, J. Smith, J. Swails, R.C. Walker, J. Wang, J. Wang, H. Wei, R.M. Wolf, X. Wu, Y. Xiong, Y. Xue, D.M. York, S. Zhao, and P.A. Kollman (2022), Amber 2022, University of California, San Francisco.

More Information

The Amber home page has an extensive manual and useful tutorials.

Last update: January 13, 2023