...

#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.

#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>.

# 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.

#!/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.

...

#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.

...

...

Checking usage

In order to determine if your process is running correctly, in each cluster you can connect directly to each compute server while you are running the file with ssh. Then use the command top to check the CPU and memory usage, rocm-smi to check the GPU usage for AMD/RADEON GPUs and nvidia-smi to check the GPU usage for NVIDIA GPUs,

Remote access to the clusters

...

You should be able to connect to the remote machine without being prompted for a password. Exit to your local machine using Ctrl+D

Create a ssh config file

...

To make it easier to connect to the remote machine in the future, you can create or edit your ssh config file in  ~/.ssh/config. This file allows you to specify connection settings and aliases for different remote machines. To create an ssh config file, open the ~/.ssh/config file in a text editor and enter the following information, replacing user_id with your username on the remote machine:

Code Block
Host crc User user_id HostName gw.crc.rice.edu IdentityFile ~/.ssh/id_rsa Host aries User user_id HostName aries.rice.edu ProxyJump crc Port 22 IdentityFile ~/.ssh/id_rsa Host nots User user_id HostName nots.rice.edu ProxyJump crc Port 22 IdentityFile ~/.ssh/id_rsa

...

To add the keys to the compute servers add the keys from your local machine to ~/.ssh/authorized_keys in the compute machine. For that in your local machine get the public key by executing the following command:

 

Code Block
language bash
cat ~/.ssh/id_rsa.pub

 

Connect from your local machine to the compute servers using the settings and alias specified in the ssh config file with the following command: 

Code Block
language bash
ssh nots

 

The compute server will ask You will be prompted for a password.

 

...

Once you have entered it, you can edit or create the ~/.ssh/authorized_keys file on the compute server using a text editor like vi. Make sure to create the folder .ssh first if it doesn't exist:

Code Block
language bash
mkdir .ssh vi ~/.ssh/authorized_keys

Add and add the contents from your ~of your local machine's ~/.ssh/id_rsa.pub file in to a new line .

...

language	bash

...

in the authorized_keys

...

file. Save the file exit the text editor (:wq) and then exit to your local machine with Ctrl+D.

...

Code Block
language bash
ssh nots

You should now be able to connect to the compute servers server without being prompted for a password.

Repeat these steps for each additional compute server you want to connect to.

More Information