Using GPUs on ShARC
Requesting access to GPU facilities
Public GPU nodes have now been made available to ShARC users, these can be be used without acquiring extra permission.
Research groups also have an option to purchase and add nodes to the cluster to be managed by IT Services. For these nodes (e.g. NVIDIA DGX-1 (Computer Science)), permission from the group leader is required for access.
The node owner always has highest priority on the job queue but
as a condition for the management of additional nodes on the cluster,
the nodes are expected to be used as a resource for running short jobs during idle time.
If you would like more information about having IT Services add and manage custom nodes,
Interactive use of the GPUs
Once you are included in the GPU project group you may start using the GPU enabled nodes interactively by typing:
qrshx -l gpu=1
-l gpu= parameter determines how many GPUs you are requesting.
Currently, the maximum number of GPUs allowed per job is set to 8.
Most jobs will only make use of one GPU.
Interactive sessions provide you with 2 GB of CPU RAM by default which is significantly less than the amount of GPU RAM available. This can lead to issues where your session has insufficient CPU RAM to transfer data to and from the GPU. As such, it is recommended that you request enough CPU memory to communicate properly with the GPU:
# NB Each NVIDIA K80 GPU has 12GB of onboard memory qrshx -l gpu=1 -l rmem=13G
The above command will give you 13GB of main memory, which is 1GB more than the 12GB of GPU memory available onboard the NVIDIA K80.
Submitting batch GPU jobs
To run batch jobs on GPU nodes, ensure your job submission script includes a request for GPUs, e.g. for a single GPU:
#!/bin/bash #$ -l gpu=1
Requesting GPUs and multiple CPU cores from the scheduler
You may want to request multiple CPU cores on a single node with a certain number of GPUs per CPU core. Here is how to request four CPU cores on a node with two GPU per CPU core:
#!/bin/bash #$ -pe smp 4 #$ -l gpu=2
It is not currently possible to request:
more CPU cores than GPUs
e.g. a heterogeneous application which could use all 40 CPU cores in a given node whilst using all 8 GPUs;
non-multiple ratios of GPUs to CPUs
e.g. a heterogeneous application which uses 4 GPUs, with 1 CPU core per GPU and can additionally use CPU cores for asynchronous host work i.e. an extra 16 cores, totalling 20 CPUs.
However, such scheduler requests may be supported in future.