Using GPUs with Slurm

For general advice on job scheduling, see Running jobs.

Available hardware

These are the node types containing GPUs currently available on Béluga, Cedar, Graham and Hélios:

# of Nodes	Node type	CPU cores	CPU memory	# of GPUs	GPU type	PCIe bus topology
172	Béluga Base GPU	40	191000M	4	NVIDIA V100-SXM2-16GB	All GPUs associated with the same CPU socket
114	Cedar Base GPU	24	128000M	4	NVIDIA P100-PCIE-12GB	Two GPUs per CPU socket
32	Cedar Large GPU	24	257000M	4	NVIDIA P100-PCIE-16GB	All GPUs associated with the same CPU socket
160	Graham Base GPU	32	127518M	2	NVIDIA P100-PCIE-12GB	One GPU per CPU socket
7	Graham Base GPU	28	183105M	8	NVIDIA V100-PCIE-16GB	Four GPUs per CPU socket
36	Graham Base GPU	16	196608M	4	NVIDIA Tesla T4 16GB	Two GPUs per CPU socket
15	Hélios K20	20	110000M	8	NVIDIA K20 5GB	Four GPUs per CPU socket
6	Hélios K80	24	257000M	16	NVIDIA K80 12GB	Eight GPUs per CPU socket

Specifying the type of GPU to use

Most clusters have multiple types of GPUs available. You can specify the type of GPU to use by adding a specifier to the --gres=gpu option. The following options are available :

On Cedar

You can request a 12G P100 using

 #SBATCH --gres=gpu:gpu:1

or a 16G P100 using

 #SBATCH --gres=gpu:lgpu:1

Unless specified, all GPU jobs requesting <= 125G of memory will run on 12G P100s

On Graham

You can request a P100 using

 #SBATCH --gres=gpu:p100:1

or a V100 using

 #SBATCH --gres=gpu:v100:1

or a T4 using

 #SBATCH --gres=gpu:t4:1

Unless specified, all GPU jobs will run on a P100

On Béluga

Béluga has only a single type of GPU, so no option can be provided.

On Hélios

You can request K20 using

 #SBATCH --gres=gpu:k20:1

or a K80 using

 #SBATCH --gres=gpu:k80:1

Single-core job

If you need only a single CPU core and one GPU:

File : gpu_serial_job.sh

#!/bin/bash
#SBATCH --account=def-someuser
#SBATCH --gres=gpu:1              # Number of GPUs (per node)
#SBATCH --mem=4000M               # memory (per node)
#SBATCH --time=0-03:00            # time (DD-HH:MM)
./program                         # you can use 'nvidia-smi' for a test

Multi-threaded job

For GPU jobs asking for multiple CPUs in a single node:

File : gpu_threaded_job.sh

#!/bin/bash
#SBATCH --account=def-someuser
#SBATCH --gres=gpu:1              # Number of GPU(s) per node
#SBATCH --cpus-per-task=6         # CPU cores/threads
#SBATCH --mem=4000M               # memory per node
#SBATCH --time=0-03:00            # time (DD-HH:MM)
export OMP_NUM_THREADS=$SLURM_CPUS_PER_TASK
./program

For each GPU requested on:

• Béluga, we recommend no more than 10 CPU cores.
• Cedar, we recommend no more than 6 CPU cores.
• Graham, we recommend no more than 16 CPU cores.

MPI job

File : gpu_mpi_job.sh

#!/bin/bash
#SBATCH --account=def-someuser
#SBATCH --gres=gpu:4              # Number of GPUs per node
#SBATCH --nodes=2                 # Number of nodes
#SBATCH --ntasks=48               # Number of MPI process
#SBATCH --cpus-per-task=1         # CPU cores per MPI process
#SBATCH --mem=120G                # memory per node
#SBATCH --time=0-03:00            # time (DD-HH:MM)
export OMP_NUM_THREADS=$SLURM_CPUS_PER_TASK
srun ./program

Whole nodes

If your application can efficiently use an entire node and its associated GPUs, you will probably experience shorter wait times if you ask Slurm for a whole node. Use one of the following job scripts as a template.

Scheduling a GPU node at Graham

File : graham_gpu_node_job.sh

#!/bin/bash
#SBATCH --nodes=1
#SBATCH --gres=gpu:2
#SBATCH --ntasks-per-node=32
#SBATCH --mem=127000M
#SBATCH --time=3:00
#SBATCH --account=def-someuser
nvidia-smi

Scheduling a Base GPU node at Cedar

File : cedar_gpu_node_job.sh

#!/bin/bash
#SBATCH --nodes=1
#SBATCH --gres=gpu:4
#SBATCH --ntasks-per-node=24
#SBATCH --exclusive
#SBATCH --mem=125G
#SBATCH --time=3:00
#SBATCH --account=def-someuser
nvidia-smi

Scheduling a Large GPU node at Cedar

There is a special group of large-memory GPU nodes at Cedar which have four Tesla P100 16GB cards each. (Other GPUs in the cluster have 12GB.) The GPUs in a large-memory node all use the same PCI switch, so the inter-GPU communication latency is lower, but bandwidth between CPU and GPU is lower than on the regular GPU nodes. The nodes also have 256 GB RAM instead of 128GB. You may only request these nodes as whole nodes, therefore you must specify --gres=gpu:lgpu:4. Note that the maximum run-time for the large-memory GPU nodes on Cedar used to be 24 hours, this is not the case any more. Large GPU jobs up to 28 days can be run on Cedar.

File : large_gpu_job.sh

#!/bin/bash
#SBATCH --nodes=1 
#SBATCH --gres=gpu:lgpu:4   
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=24    # There are 24 CPU cores on Cedar GPU nodes
#SBATCH --mem=0               # Request the full memory of the node
#SBATCH --time=3:00
#SBATCH --account=def-someuser
hostname
nvidia-smi

Packing single-GPU jobs within one SLURM job

If you need to run four single-GPU programs or two 2-GPU programs for longer than 24 hours, GNU Parallel is recommended. A simple example is given below:

cat params.input | parallel -j4 'CUDA_VISIBLE_DEVICES=$(({%} - 1)) python {} &> {#}.out'

In this example the GPU ID is calculated by subtracting 1 from the slot ID {%}. {#} is the job ID, starting from 1.

A params.input file should include input parameters in each line like:

code1.py
code2.py
code3.py
code4.py
...

With this method, users can run multiple tasks in one submission. The -j4 parameter means GNU Parallel can run a maximum of four concurrent tasks, launching another as soon as each one ends. CUDA_VISIBLE_DEVICES is used to ensure that two tasks do not try to use the same GPU at the same time.

Message Passing Interface