Slurm Access to the Cori GPU nodes¶
The GPU nodes are accessible via Slurm on the Cori login nodes. They are
exposed as a hardware 'constraint', in the same way that the Haswell and KNL
compute nodes are. They require that you use the
esslurm module before you
run your Slurm scripts, or else your jobs will fail.
Each node has 8 GPUs, 40 CPU cores spread across 2 sockets with 2 hyper-threads per core, and 384 GB DRAM. To access approximately 1/8 of a single node's resources (generally sufficient for single-GPU code development), one can execute
user@cori02> module load esslurm user@cori02> salloc -C gpu -N 1 -t 60 -c 10 --gres=gpu:1 -A m1759 salloc: Granted job allocation 12345 salloc: Waiting for resource configuration salloc: Nodes cgpu02 are ready for job user@cgpu02:~>
which will provide the user with 1 GPU, 5 physical cores (10 hyper-threads),
and approximately 30 GB of DRAM. Note that Slurm already allocates memory to
your job proportial to the number of CPUs you request for your job. E.g., if
-c 40 (half of the available CPUs), you will be allocated roughly
half of the memory on the node - approximately 192 GB.
The new flag in the above example which is not used elsewhere on Cori is
--gres, which is used to reserve a particular number of GPUs on the node.
GPU nodes are 'shared' by default
Slurm's default behavior on the 'normal' compute nodes on Cori and Edison
is to reserve each compute node entirely for yourself; every node in your
job allocation is exclusively yours. However, on the GPU nodes, the default
behavior is the opposite - the default behavior is to share the nodes in
your job allocation with other users. If you need to reserve all CPU
resources on a node for yourself, you can specify the
in your Slurm script invocation.
Although sharing nodes reduces the likelihood that you will need to wait for a node to become available, users of shared nodes may encounter signficant performance variability due to other concurrent activity on the node, particularly if PCI traffic (CPU <-> GPU memory bandwidth and network bandwidth) comprises a significant portion of an application's performance. This is because the GPU nodes do not have enough PCI bandwidth to service all PCI connections at full speed.
Use only what you need
There are only 18 GPU nodes to satisfy the development needs of many NERSC users. If you need all CPUs and GPUs on a given number of GPU nodes for your work, you should use them. But if you only need a single GPU and a single physical core, please be mindful of others and do not reserve the entire node for yourself.
Job constraints are as follows:
- Jobs requesting <= 2 nodes must request <= 4 hours.
- Jobs requesting > 2 nodes must request > 4 hours.
- Batch jobs (but not interactive jobs) may violate the above constraints by
submitting directly to the
gpu_preemptQoS. (See details below.)
Jobs in the second category, requesting > 2 nodes and > 4 hours, will be placed
in a "preemptable" queue. Preemptable
jobs are a special type of job in Slurm, which can be stopped in order to allow
a higher priority job in the queue to start, even while the preemptable job is
currently executing. In the case of Cori GPU, jobs requesting <= 2 nodes and <=
4 hours of run time have higher priority than jobs requesting > 2 nodes and > 4
hours. A job which is preempted will print a message to
STDERR similar to the
message printed when a job is canceled due to exceding a time limit, except
that the reason for the cancellation will be
One can mitigate the disruption due to job preemption by using two strategies:
- Ensure that the long-running code checkpoints frequently.
- Add the
--requeueflag to the job's submission script, so that it is automatically resubmitted to the queue if it is preempted.
If the user wishes to run a batch job which requires > 4 hours, but requires
fewer than 2 nodes (perhaps even just a single GPU on one shared node), this
can be achieved by submitting the job directly to the
gpu_preempt QoS, e.g.,
#!/bin/bash #SBATCH -C gpu #SBATCH -q gpu_preempt #SBATCH -t 300 #SBATCH -c 10 #SBATCH --gres=gpu:1 #SBATCH -A m1759 #SBATCH --requeue srun -n 1 ./my.exe
Note that this works only for batch jobs - interactive jobs via
not able to submit directly to the
Each user is enabled on the GPU nodes via the m1759 repository. If that
repository is not your default for MPP charging, you must specify that repo
-A m1759 when you access the GPU nodes, or else your job
submission will fail.
Slurm commands with
esslurm module is loaded, commands such as
sbatch, etc. will not show information or submit jobs to 'normal' Cori
compute nodes. To query the 'normal' compute nodes, unload the
module unload esslurm and then enter your desired Slurm