16 Linux: Gaussian

Here is comprehensive content regarding Gaussian 16 for Linux, structured as a technical guide or informational article. This covers everything from system requirements and installation to running jobs and common Linux commands.

4. Using Multiple Cores & Memory

Gaussian 16 supports shared-memory parallelism via OpenMP and distributed-memory via Linda (for separate license). gaussian 16 linux

| Directive | Purpose | |-----------|---------| | %nprocshared=8 | Use 8 CPU cores | | %mem=16GB | Allocate 16 GB RAM | | %LindaWorkers=node1:2,node2:2 | Distributed across nodes (requires Linda) | Here is comprehensive content regarding Gaussian 16 for

🧠 Set %mem slightly below physical RAM to leave room for OS. 🧠 Set %mem slightly below physical RAM to

Load Gaussian module (if provided by admin)

module load gaussian/16

3.1 Prerequisites

# For RHEL/CentOS/Rocky/Alma
sudo dnf install -y csh tcsh libnsl libXt libXext libXrender libXmu libXp
3. Running a Gaussian 16 Job
Docker and Containerization
For reproducible research, you can containerize Gaussian 16 using Docker or Singularity. Example Dockerfile (simplified):
FROM ubuntu:20.04
RUN apt update && apt install -y csh libgfortran4
COPY g16 /opt/g16
ENV g16root=/opt
ENV GAUSS_EXEDIR=/opt/g16
ENV PATH=/opt/g16:$PATH

Build and run:
docker build -t g16 .
docker run --rm -v $(pwd):/data -w /data g16 g16 input.com output.log

Step 1: Extract the Software
sudo mkdir -p /opt/g16
sudo chown -R $USER:$USER /opt/g16
tar -xzvf G16-A.03.tgz -C /opt/g16