GPU Queues

There is a number of different (public) partitions (SLURM lingo for 'queues') inside the cluster that support gpu usage:

Notes:

• RAM displays the default memory per node in MiB.

To get to know which account to use for the m2_gpu partition login and call

sacctmgr list user $USER -s where Partition=m2_gpu format=User%10,Account%20,Partition%10

All accounts that show Partition=m2_gpu can be used to submit jobs to the GPU-Partition.

The m2_gpu is a single partition allowing a runtime of up to 5 days. In order to prevent single users or groups to flood the entire partition with their long running jobs, a limitation has been set, such that other users get the chance to run their jobs, too. This may result in pending reasons such as QOSGrpGRESRunMinutes. For other pending reasons, see our page on job management.

There is a partition m2_gpu-compile which allows for running one job per user with maximum 8 cores, 1 cpu, and –mem=18000M for compiling your code. Maximum runtime for compile jobs is 60 minutes.

To use a GPU you have to explicitly reserve it as a resource in the submission script:

#!/bin/bash
# ... other SBATCH statements
#SBATCH --gres=gpu:<number>
#SBATCH -p <appropriate partition>

Number can be anything from 1-6 on our GPU nodes, depending on the partition. In order to use more than 1 GPU the application needs to support using this much, of course.

Take a single GPU-node and run an executable on it ¹⁾.

#!/bin/bash
#-----------------------------------------------------------------
# Example SLURM job script to run serial applications on Mogon.
#
# This script requests one task using 2 cores on one GPU-node.  
#-----------------------------------------------------------------
 
#SBATCH -J mysimplegpujob        # Job name
#SBATCH -o mysimplegpujob.%j.out # Specify stdout output file (%j expands to jobId)
#SBATCH -p m2_gpu                # Partition name
#SBATCH -n 1                     # Total number of tasks 
#SBATCH -c 2                     # CPUs per task 
#SBATCH -t 00:30:00              # Run time (hh:mm:ss) - 0.5 hours
#SBATCH --gres=gpu:1             # Reserve 1 GPUs 
#SBATCH -A m2_account            # Specify allocation to charge against
 
# Load all necessary modules if needed (these are examples)
# Loading modules in the script ensures a consistent environment.
module load system/CUDA
 
# Launch the executable
srun <myexecutable>

Take a full GPU-node and run an executable that uses all 6 GPUs ²⁾.

#!/bin/bash
#-----------------------------------------------------------------
# Example SLURM job script to run serial applications on Mogon.
#
# This script requests one task using all cores (48) on one node. 
# The job will have access to all the memory and all 6 GPUs in the node.  
#-----------------------------------------------------------------
 
#SBATCH -J mysimplegpujob        # Job name
#SBATCH -o mysimplegpujob.%j.out # Specify stdout output file (%j expands to jobId)
#SBATCH -p m2_gpu                # Partition name
#SBATCH -N 1                     # Total number of nodes requested (48 cores/node per GPU node)
#SBATCH -n 1                     # Total number of tasks 
#SBATCH -c 48                    # CPUs per task 
#SBATCH -t 00:30:00              # Run time (hh:mm:ss) - 0.5 hours
#SBATCH --gres=gpu:6             # Reserve 6 GPUs 
 
#SBATCH -A m2_account            # Specify allocation to charge against
 
# Load all necessary modules if needed (these are examples)
# Loading modules in the script ensures a consistent environment.
module load system/CUDA
 
# Launch the executable
srun <myexecutable>

Take a full GPU-node and run 6 executables each on one GPU.

#!/bin/bash
#-----------------------------------------------------------------
# Example SLURM job script to run serial applications on MOGON.
#
# This script requests one task using all cores (48) on one node. 
# The job will have access to all the memory and all 6 GPUs in the node.  
#-----------------------------------------------------------------
 
#SBATCH -J mysimplegpujob        # Job name
#SBATCH -o mysimplegpujob.%j.out # Specify stdout output file (%j expands to jobId)
#SBATCH -p m2_gpu                # Partition name
#SBATCH -N 1                     # Total number of nodes requested (48 cores/node per GPU node)
#SBATCH -n 6                     # Total number of tasks 
#SBATCH -c 8                     # CPUs per task 
#SBATCH -t 00:30:00              # Run time (hh:mm:ss) - 0.5 hours
#SBATCH --gres=gpu:6             # Reserve 6 GPUs 
 
#SBATCH -A m2_account            # Specify allocation to charge against
 
# Load all necessary modules if needed (these are examples)
# Loading modules in the script ensures a consistent environment.
module load system/CUDA
 
# Launch the tasks
GPUTASKS=$(grep -o ',' <(echo $SLURM_JOB_GPUS) | wc -l)
for ((i=0; i<=GPUTASKS; i++))
do
   echo "TASK $i"
   srun -n 1 -c $SLURM_CPUS_PER_TASK --exclusive --gres=gpu:1 --mem=18G <executable> & 
done
 
wait

Most GPU programs just know which device to select. Some do not. In any case SLURM exports the environment variable CUDA_VISIBLE_DEVICES, which simply holds the comma-separated, enumerated devices allowed in a job environment, starting from 0.

So, when for instance another job occupies the first device and your job selects two GPUs, CUDA_VISIBLE_DEVICES might hold the value 1,2 and you can read this into an array³⁾:

#good practice is to store the initial IFS setting:
IFSbck=$IFS
IFS=',' read -a devices <<< $CUDA_VISIBLE_DEVICES
IFS=$IFSbck # in case it is used in subsequent code

Now, you can point your applications to the respective devices (assuming you start two and not one, which uses both):

cmd --argument_which_receives_the_device ${devices[0]} & # will hold the 1st
cmd --argument_which_receives_the_device ${devices[1]} & # will hold the 2nd