: Defines the number of CPU cores assigned to the job.
Mastering Gaussian 16 on Linux involves a holistic approach: proper installation, precise configuration, efficient resource allocation, and systematic troubleshooting. By selecting the correct binary for your CPU, optimizing memory and core usage, and leveraging GPU acceleration where beneficial, you can achieve significant performance gains. For large-scale projects, adopting the best practices for HPC job scheduling and temporary file management is essential.
export g16root=/share/home/kanghao/soft export GAUSS_SCRDIR=/share/home/kanghao/soft/g16/scratch source $g16root/g16/bsd/g16.profile
Increase GAUSS_SCRDIR space or use %chk to save to a different location. gaussian 16 linux
After submitting, you can safely exit the terminal with the exit command. The job will continue in the background.
: Add source $g16root/g16/bsd/g16.profile to initialize the environment.
export GAUSS_SCRDIR=/local/scratch/$SLURM_JOB_ID mkdir -p $GAUSS_SCRDIR : Defines the number of CPU cores assigned to the job
sudo nano /etc/profile.d/gaussian.sh
export g16root=/usr/local export GAUSS_SCRDIR=/scratch/g16_scratch source $g16root/g16/g16.login Use code with caution.
The Gaussian input file ( test.com ) remains platform-agnostic, but the submission method differs drastically on Linux. For large-scale projects, adopting the best practices for
source ~/.bashrc g16 < /dev/null
: Specifies the name of the checkpoint file, saving molecular orbitals and Hessian matrices.