Getting started with GPUs
The m9g cluster has 40 nodes with GPUs, each with 4 NVIDIA P100 GPUs.
The m8g cluster has 32 nodes with GPUs, each with 2 NVIDIA K80's. Note that the devices report as 2 GPUs each, however. This means if you query the system it will report that there are 4 K80 GPUs available.
Each node has 64 GB of memory; the CPUs match the m8 cluster exactly. The NVIDIA K80's are a substantial increase in computing power compared to our previous GPUs. For more detailed information about the GPU hardware, see NVIDIA's website.
Using GPUs in Jobs
You can request GPUs in Slurm using the
--gres feature. As an
example you can request a whole node and its 4 GPUs by including the
following in your
--nodes=1 --mem=64G --exclusive --gres=gpu:4. Please be aware
of how many GPUs your program can use and request accordingly. If you can
only use one GPU and 6 processors use:
--nodes=1 --ntasks=6 --mem=12G --gres=gpu:1. The environment
CUDA_VISIBLE_DEVICES will contain a comma separated
list of CUDA devices that your job has access to.
To compile or run CUDA code you'll need the CUDA libraries and runtime; get
them in your path by doing:
module load cuda.
For interactive development or for compiling CUDA programs, you will need to
request an interactive job using
program accepts the same flags as
sbatch but you must provide
them on the command-line since
salloc isn't given a file to run
(it gives you a shell instead).
Here are our current GPU restrictions:
- Walltime of 3 days
- No more than 26 GPU nodes total
- No more than 26 GPU jobs at any time
- Your jobs can escape all limits (up to 7 days walltime) by becoming preemptable: --qos=standby
These are subject to change, and administrators may impose additional restrictions as they see fit based on demand.