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It should be noted that this example assumes the use of only one GPU per task and requests an equal amount of memory and CPU resources based on the total resources of each node. The amount of CPU and RAM memory utilized can be increased or decreased based on the user's experience with their system.

NOTS (commons)

 This partition includes 16 volta GPU nodes, each equipped with 80 CPUs and 182GB of RAM. In addition, each node includes 2 NVIDIA GPUs.

##SBATCH --account=commons
#SBATCH --partition=commons
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=40
#SBATCH --mem=90G
#SBATCH --gres=gpu:1

NOTS (ctbp-common)

This partition includes two ampere GPU nodes, each 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-common
#SBATCH --partition=ctbp-common
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=2
#SBATCH --mem=64G
#SBATCH --gres=gpu:1

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

ARIES

 This partition includes x 19 GPU nodes, each equipped with an AMD EPYC chip featuring 48 CPUs and 512GB of RAM. In addition, each node includes 8 GPUs per node, which must be utilized in parallel. To 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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PODS

This partition includes x 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

Accessing the cluster

To access the servers from outside of Rice it is recommended to connect to them through the gw.crc.rice.edu server. Here we will show how to create a passwordless ssh tunnel that will allow you to securely connect to a remote machine without having to enter a password everytime you connect.

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