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    Amber

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

    Available

    • Puhti: 18, 18-cuda, 20, 20-cuda

    License

    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.

    Usage

    Start using the AmberTools with the default version:

    module load amber

    Use module spider amber to see all available versions. 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).

    Molecular dynamics jobs are best run with pmemd.CUDA. They are much faster on GPGPUs than on CPUs. Please note, that using pmemd.CUDA requires a different module amber/20-cuda, but it does not have all the AmberTools available.

    Note

    Run only GPU aware binaries in the gpu partition. If you're unsure, check with seff that GPU has been used, and the job was significantly faster than without GPUs.

    Our tests show that for moderate sized systems the most efficient setup is one V100 GPGPU card and one CPU core. An example batch script 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 GPGPU

module load amber/20-cuda

srun --gres=gpu:v100:1 pmemd.cuda -O -i mdin -r restrt -x mdcrd -o mdout

    Note

    If you want to use more than one GPGPU, perform scaling tests to verify that the jobs really become faster. The rule of thumb is that when you double the resources, the job duration should shrink at least 1.5 fold. For overall performance info, consult Amber benchmark scaling info.

    You can find example inputs from the amber tests directory:

    ls $AMBERHOME/test

    The non-CUDA aware binaries, e.g. AmberTools can be run as batch jobs e.g. with the following way:

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

# 1 task

module load amber/20

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

    Note

    pmemd.CUDA is way faster than pmemd.MPI, so use a CPU-only version only in case you cannot use the CUDA version. If Amber performance is not fast enough, consider using Gromacs, which can make use of more CPU cores (i.e. scales further) can be (while using more resources) an order of magnitude faster. In particular, for large scale or very long MD simulations consider using a better scaling MD engine.

    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 with sinteractive or manually access to a single core with:

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

    Then, once you have the resources (you might need to wait), you can run the paramfit task directly with:

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

    References

    When citing Amber20 or AmberTools20 please use the following:

    D.A. Case, K. Belfon, I.Y. Ben-Shalom, S.R. Brozell, D.S. Cerutti, T.E. Cheatham, III, 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, A. Kovalenko, R. Krasny, T. Kurtzman, T.S. Lee, S. LeGrand, P. Li, C. Lin, J. Liu, T. Luchko, R. Luo, V. Man, K.M. Merz, Y. Miao, O. Mikhailovskii, G. Monard, H. Nguyen, A. Onufriev, F.Pan, S. Pantano, R. Qi, D.R. Roe, A. Roitberg, C. Sagui, S. Schott-Verdugo, J. Shen, C.L. Simmerling, N.R.Skrynnikov, J. Smith, J. Swails, R.C. Walker, J. Wang, L. Wilson, R.M. Wolf, X. Wu, Y. Xiong, Y. Xue, D.M. York and P.A. Kollman (2020), AMBER 2020, University of California, San Francisco.

    For Amber 2018:

    D.A. Case, I.Y. Ben-Shalom, S.R. Brozell, D.S. Cerutti, T.E. Cheatham, III, V.W.D. Cruzeiro, T.A. Darden, R.E. Duke, D. Ghoreishi, M.K. Gilson, H. Gohlke, A.W. Goetz, D. Greene, R Harris, N. Homeyer, S. Izadi, A. Kovalenko, T. Kurtzman, T.S. Lee, S. LeGrand, P. Li, C. Lin, J. Liu, T. Luchko, R. Luo, D.J. Mermelstein, K.M. Merz, Y. Miao, G. Monard, C. Nguyen, H. Nguyen, I. Omelyan, A. Onufriev, F. Pan, R. Qi, D.R. Roe, A. Roitberg, C. Sagui, S. Schott-Verdugo, J. Shen, C.L. Simmerling, J. Smith, R. Salomon-Ferrer, J. Swails, R.C. Walker, J. Wang, H. Wei, R.M. Wolf, X. Wu, L. Xiao, D.M. York and P.A. Kollman (2018), AMBER 2018, University of California, San Francisco.

    More Information

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

    Last edited Tue Apr 5 2022