- Interactive sessions for multi-task applications
- Interactive sessions for single task applications on a single node
- Interactive sessions on a GPU node
- Development partitions
The primary focus of the Sulis service is non-interactive processing of scripts submitted to the batch system from the login node. Despite this there are occasions when interactive access to a compute node can be beneficial for debugging, testing and compiling.
SLURM can be used to allocate resources for interactive work, but note the following caveats.
There is no guarantee that the resources will be available at a convenient time if the cluster is busy.
Interactive jobs consume CPU and GPU time budgets in the same way as batch jobs. Leaving an interactive session idle when not in use can consume a significant fraction of a user’s allocation.
Should you have difficulty obtaining an interactive session at a useful time then is may be possible to make an advance reservation. Contact your local support representative to request this.
From the login node, issue the following
salloc command to request an interactive session on a compute node. This assumes that any task parallel (e.g. MPI) operations launched within the session will use a single CPU per task, so we request 128 “slots” available for processes in the session. This is the appropriate configuration for debugging a pure MPI code.
[user@login01(sulis) ~]$ salloc --account=suxxx-somebudget -N 1 -n 128 --mem-per-cpu=3850 --time=8:00:00
You will see the message
salloc: job xxxxxx queued and waiting for resources until a compute node becomes available. Once allocated the user will be dropped into an interactive session on that node, in this case node046.
The interactive session will end after the 8 hours requested, or if the user ends the session with:
[user@node046(sulis) ~]$ exit
To launch MPI applications within this session use
mpirun) as you would within a SLURM job script. This will use all 128 slots unless overridden with the
-n argument, for example:
[user@node046(sulis) ~]$ srun -n 16 ./my_mpi_prog
It is possible to use
salloc to request resource across multiple nodes, and then use
srun to launch processes which use that allocation. This can be useful for debugging problems with code/scripts that only manifest when using multiple nodes.
For example, the following requests an allocation of two nodes,
[user@login01(sulis) ~]$ salloc --account=suxxx-somebudget --ntasks-per-node=1 --cpus-per-task=128 --mem-per-cpu=3850 --nodes=2 --time=00:15:00
Once the allocation has been granted, and interactive shell will be started on the first allocated node. Using
srun will (in this case) execute 1 instance of the command specified on each of the two nodes allocated. For example:
[user@node004(sulis) ~]$ srun uname -n node004.sulis.hpc node005.sulis.hpc
exit to relinquish the allocation when no longer needed.
[user@node004(sulis) ~]$ exit exit salloc: Relinquishing job allocation 223826
From the login node
[user@login01(sulis) ~]$ salloc --account=suxxx-somebudget -N 1 -n 1 -c 128 --mem-per-cpu=3850 --time=8:00:00
will (as above) display
salloc: job xxxxxx queued and waiting for resources until a compute node becomes available. Once given a terminal prompt on the allocated node,
srun can be used to launch a single process which has access to all 128. This is the appropriate configuration for debugging a threaded code or Python script that uses multiprocessing. For example the following will launch a Python script which can use all 128 CPUs in the node.
[user@node046(sulis) ~]$ srun -n 1 -c 128 python my_multiproc_script.py
See also the application notes on Jupyter.
To request an interactive session on a GPU-equipped node, specify the
gpu partition and include a resource request for the A100 GPUs in that node.
[user@login01(sulis) ~]$ salloc --account=suxxx-somebudget -p gpu -N 1 -n 1 -c 128 --mem-per-cpu=3850 --gres=gpu:ampere_a100:3 --time=8:00:00
In this case SLURM will allocate one “slot” for a process launched via
srun which will have access to all 128 CPUs and the 3 GPUs in the node. Once the resources are allocated an available then a new command prompt will appear.
This environment will have access to all 3 GPUs in the node.
[user@gpu016(sulis) ~]$ nvidia-smi -L GPU 0: A100-PCIE-40GB (UUID: GPU-04a0384d-82ee-c4c5-2414-4ad1ab396f49) GPU 1: A100-PCIE-40GB (UUID: GPU-f55b44e4-79e9-0ee0-c1ad-a4dbd693ad71) GPU 2: A100-PCIE-40GB (UUID: GPU-23d2b9d2-42d7-3886-a921-5c4869914b0e)
If the machine is busy you may wish to submit interactive jobs to the
devel partition (or
gpu-devel for GPU jobs) which has higher queue priority but a maximum walltime of one hour.