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| Code Block |
|---|
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#SBATCH --account=ctbp-common
#SBATCH --partition=ctbp-common
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=2
#SBATCH --mem=64G
#SBATCH --gres=gpu:1
ml gomkl/2021a OpenMM/7.7.0-CUDA-11.4.2 |
NOTS (ctbp-onuchic)
This partition includes one GPU node, equipped with an AMD EPYC chip featuring 16 CPUs and 512GB of RAM. In addition, each node includes 8 NVIDIA A40 GPUs with 48GB of memory.
| Code Block |
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#SBATCH --account=ctbp-onuchic
#SBATCH --partition=ctbp-onuchic
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=2
#SBATCH --mem=64G
#SBATCH --gres=gpu:1
ml gomkl/2021a OpenMM/7.7.0-CUDA-11.4.2 |
OpenMM on NOTS
You can deploy and run you own version of OpenMM via conda environment. For that, first install the OpenMM inside a conda environment requesting the modules already installed on NOTS. Note that in order to run with Nvidia GPUs, it has to be complicated with CUDA/<version>.
| Code Block |
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| language | bash |
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| title | Conda environment with OpenMM |
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# Load conda and gpu modules
module load Anaconda3/2022.05 CUDA/11.4.2
# Create the openmm environment
conda create --prefix $HOME/openmm
# Activate the new env.
source /opt/apps/software/Anaconda3/2022.05/bin/activate
conda activate $HOME/openmm
# Then install OpenMM. You can also follow by installing your favorite MD wrapper
conda install -c conda-forge openmm cudatoolkit=11.4.2 h5py openmichrom opensmog |
This would be an example of a running slurm script.
| Code Block |
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| language | bash |
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| title | Slurm running OpenMM via environment |
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#!/bin/bash -l
#SBATCH --account=ctbp-common
#SBATCH --partition=ctbp-common
#SBATCH --job-name=Template-OPENMM
#SBATCH --ntasks=1
#SBATCH --threads-per-core=1
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=2G
#SBATCH --gres=gpu:1
#SBATCH --time=00:05:00
#SBATCH --export=ALL
module purge
module load Anaconda3/2022.05 CUDA/11.4.2
source /opt/apps/software/Anaconda3/2022.05/bin/activate
conda activate $HOME/openmm
python your_script.py |
ARIES
This partition includes 19 GPU nodes, each equipped with an AMD EPYC chip featuring 48 CPUs and 512GB of RAM. In addition, each node includes 8 AMD MI50 GPUs with 32 GB of memory each. To submit a job to this queue, it is necessary to launch 8 processes in parallel, each with a similar runtime to minimize waiting time. This ensures that all of the GPUs are used efficiently.
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