GPU-ACCELERATED GAMESS

Get Started Today with This GPU-Ready Apps Guide.

GAMESS

General Atomic and Molecular Electronic Structure Systems (GAMESS) is a software application for simulating molecular quantum chemistry, allowing users to calculate various molecular properties and dynamics.

GAMESS can perform several general computational chemistry calculations, including the Hartree-Fock method, density functional theory (DFT), generalised valence bond (GVB), and multi-configurational self-consistent field (MCSCF). Correlation corrections after these self-consistent field (SCF) calculations can be estimated by configuration interaction (CI), second-order Møller-Plesset perturbation theory (MP2), and coupled-cluster (CC) theory. Solvent effect can be considered using quantum mechanics and molecular mechanics through discrete effective fragment potentials or continuum models (such as the polarizable continuum model [PCM]). Relativistic corrections can be calculated, including third-order Douglas-Kroll scalar terms.

More info on GAMESS can be found here.

Installation

A site license for GAMESS is available at no cost to both academic and industrial users, and more information can be found on the license page.

You have the option to download the source code and install GAMESS on bare-metal or pull and run the GAMESS container from NVIDIA GPU Cloud.

Installing applications in a high performance computing (HPC) environment can be challenging. Containers let you run the application without installing it on the system, making it easy to deploy the most recent version of the application while optimising performance.

Running GAMESS through containers is very straightforward and can be set up in minutes.

RUNNING JOBS

Once you pull the GAMESS container from NGC, there are two ways to run it.

  • Run GAMESS in detached mode from the nvidia-docker run command.
  • Start the container in interactive mode and run the GAMESS container interactively within the container.

1. Running GAMESS in Detached Mode

For example, to run the benchmarked RI-MP2 Valinomycin simulations in detached mode, execute the following command:

nvidia-docker run -v $(pwd):/results --rm nvcr.io/hpc/gamess:17.09-r2-libcchem -c "cd /workspace/examples && rungms cc-h2co.inp"

2. Running GAMESS in Interactive Mode

nvidia-docker run -v $(pwd):/results --rm -it nvcr.io/hpc/gamess:17.09-r2-libcchem
cd /workspace/examples
rungms rimp2-valinomycin.spherical.energy.ccd_cct.inp

Note that any simulation will have the following output:

$ [Running input $JOB on $NCPUS node(s) with $NGPUS gpu(s)]
$ [Run completed]

Upon completion, a logfile will be written to /results/ (container) and the present working directory $(pwd) (host) and will contain all simulation data.

3. Running a Simulation with Your Own Input Deck

You can provide your own input decks by mapping /path/to/your_workspace on your host machine to /mnt/workspace in the container. See below for an example of how to do this with an interactive session:

$ nvidia-docker run -v /path/to/your_workspace:/mnt/workspace -v /path/to/resultsdir:/results --rm -it nvcr.io/hpc/gamess:17.09-r2-libcchem

The run-script assumes the file structure below for your own jobs:

 

/path/to/your_workspace

/scratch/

/restart/

/your_input.inp 

To run a job using your own data, you’ll have to navigate to /path/to/your_workspace before running rungms:

$ cd /mnt/your_workspace

$ rungms your_input.inp

Benchmarks

This section shows typical performance of a GAMESS container on GPU-accelerated systems.

tesla-gpu-ready-apps-gamess-pascal-benchmark-chart-625-udt-r4
tesla-gpu-ready-apps-gamess-volta-benchmark-chart-625-udt-r4

RECOMMENDED SYSTEMS CONFIGURATIONS

The GAMESS container is optimised and tested for reliability to run on NVIDIA® Pascal- and NVIDIA Volta-powered systems with NVIDIA CUDA 9 or newer. GAMESS and all the HPC application containers available on NVIDIA GPU Cloud can run on the following systems:

  • Workstation: Powered by NVIDIA Titan V and x86 CPU
  • NVIDIA DGX Station
  • NVIDIA DGX-1
  • HPC cluster with Pascal/Volta GPUs, CUDA 9, x86 CPU
  • Cloud: Amazon Web Services

GET ACCESS TO GPU-ACCELERATED APPLICATION CONTAINERS WITH NGC.