GPUs on QUEST

What GPUs are available on QUEST?

There are 9 GPU nodes available to the Quest General Access allocations. These nodes run CUDA version 11.4 and Driver Version: 470.57.02:

• 2 x 16GB Tesla P100 GPUs available on one node (28 CPU cores, 128 GB RAM)
• 16 x 40GB Tesla A100 GPUs available on 8 nodes (two GPUs, 52 CPU cores, and 192 GB RAM on each node)

Using General Access GPUs

The maximum run time is 48 hours for a job on these nodes. To submit jobs to general access GPU nodes, you should set gengpu as the partition and state the number of GPUs in your job submission command or script. You can also identify the type of GPU you want in your job submission. For instance to request one A100 GPU, you should add the following lines in your job submission script:

#SBATCH -A <allocationID>
#SBATCH -p gengpu
#SBATCH --gres=gpu:a100:1
#SBATCH -N 1
#SBATCH -n 1
#SBATCH -t 1:00:00
#SBATCH --mem=XXG

Note that the memory you request here is for CPU memory. You are automatically given access to the entire memory of the GPU, but you will also need CPU memory as you will be copying memory from the CPU to the GPU.

To schedule another type of GPU, e.g. P100, you should change the a100 designation to the other GPU type, e.g. p100.

Using Genomics Compute Cluster GPUs

#SBATCH -A b1042
#SBATCH -p genomics-gpu
#SBATCH --gres=gpu:a100:1
#SBATCH -N 1
#SBATCH -n 1
#SBATCH -t 1:00:00
#SBATCH --mem=XXG

Note that the memory you request here is for CPU memory. You are automatically given access to the entire memory of the GPU, but you will also need CPU memory as you will be copying memory from the CPU to the GPU.

Interactive GPU jobs

What GPU software is available on QUEST?

CUDA

To see which versions of CUDA are available on Quest, run the command:

module spider cuda

NOTE: You cannot use code or applications which require a CUDA toolkit or module that is newer than the CUDA versions listed above. However, CUDA modules and toolkits that are older than the CUDA versions listed at the top of this page should still work.

Anaconda

We strongly encourage people to use anaconda to create virtual environments in order to use software that utilize GPUs, especially when using Python. Please see Using Python on QUEST for more information on anaconda virtual environments. Below we provide instructions for creating a local anaconda virtual environment containing a.) the Tensorflow and b.) the PyTorch package. Please run the command that come after the $.

Tensorflow

1. Load anaconda on QUEST

$ module load python-anaconda3

2. Create a virtual environment and install cudnn and Python into it. We are going to name our environment tensorflow-2.6-py38 . On QUEST, by default, all anaconda environments go into a folder in your HOME directory called ~/.conda/envs/. Therefore, once these steps are completed, all of the necessary packages will live in a folder whose PATH is ~/.conda/envs/tensorflow-2.6-py38

$ conda create -n tensorflow-2.6-py38 -c conda-forge -c anaconda python=3.8 cudnn=8.2.1 --yes

3. Activate virtual environment