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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 22 GPU nodes and 2 High Memory CPU nodes:

• 19 x MI50 Nodes (gn01-gn19): 1x AMD EPYC 7642 processor (96 CPUs), 512GB RAM, 2TB storage, HDR Infiniband, 8x AMD Radeon Instinct MI50 32GB GPUs.
• 3x MI100 Nodes (gn20-gn22): 2x AMD EPYC 7V13 processors (128 CPUs), 512GB RAM, 2TB storage, HDR Infiniband, 8x AMD Radeon Instinct MI100 32GB GPUs
• 2x Large Memory Nodes (hm01-02): 2x AMD EPYC 7302 processors (64 CPUs), 4TB RAM, 4TB storage, HDR Infiniband.

To submit a job to GPU 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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#SBATCH --account=commons
#SBATCH --partition=commons
#SBATCH --ntasks=8
#SBATCH --cpus-per-task=6
#SBATCH --threads-per-core=1
#SBATCH --mem-per-cpu=3G
#SBATCH --gres=gpu:8
#SBATCH --time=24:00:00
#SBATCH --export=ALL
 
module load foss/2020b OpenMM

PODS

This partition includes 80 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.

#SBATCH --account=commons
#SBATCH --partition=commons
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --export=ALL
#SBATCH --gres=gpu
 
module load foss/2020b OpenMM

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