GAMESS-US

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The General Atomic and Molecular Electronic Structure System (GAMESS) is a general ab initio quantum chemistry package.

Running GAMESS

Job Submission

Our Clusters are using the Slurm scheduler; for details about submitting jobs, see Running jobs.

First step is to prepare a GAMESS input file containing the molecular geometry and run parameters. Please refer to the GAMESS Documentation and particularly Chapter 2 "Input Description" for a description the file format and all available keywords.

Besides your input file (in our example name.inp), you have to prepare a job script to define the compute resources for the job; both input file and job script must be in the same directory.

File : gamess_job.sh

#!/bin/bash
#SBATCH --cpus-per-task=1       # Number of CPUs
#SBATCH --mem-per-cpu=2000M     # memory per CPU in MB
#SBATCH --time=0-00:30          # time (DD-HH:MM)

export SLURM_CPUS_PER_TASK
## uncomment the following 2 lines to use network $SCRATCH
#export USRSCR="$SCRATCH/gamess_${SLURM_JOB_ID}/"
#mkdir -p $USRSCR

module load gamess-us/20170420-R1

rungms name.inp  &>  name.out

Running GAMESS on multiple CPUs

GAMESS calculations can make use of more than one CPU. The number of CPUs used for a calculation is controlled by the --cpus-per-task setting in the submission script. As GAMESS has been built using the "sockets" parallelization, it can only use CPU cores that are located on the same compute node and therefore the maximum number of CPU cores that can be used for a job is dictated by the node configuraion of the system (e.g. 32 CPU cores per node on Graham).

Quantum chemistry calculations are known to not scale as well across many CPUs as compared to e.g. classical molecular mechanics, which means that they can't use large numbers of CPUs efficiently. Exactly how many CPUs can be utilized efficiently, depends on the size of a system (i.e. number of atoms, number of basis functions and level of theory).

To determine a reasonable number of CPUs to use, one needs to run a scaling test - that is running the same input file using different numbers of CPUs and comparing the execution time. Ideally the exection time should be half as long when using twice as many CPUs (= 100% speedup). Obviously it is not a good use of resources when a calculation runs only 30% faster when doubling the number of CPUs and in extreme cases calculations can become even slower when further increasing the number of CPUs.