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Using GPUs on Iceberg

Requesting access to GPU facilities

Public GPU nodes have now been made available to Iceberg and 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 CiCs. For these nodes (e.g. Nvidia DGX-1 [COM]), permission from the group leader is required for access.

The node owner always have 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 CiCs add and manage custom nodes, please contact

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:

qsh -l gpu=1

the -l gpu= parameter determines how many GPUs you are requesting. Currently, the maximum number of GPUs allowed per job is set to 4, i.e. you cannot exceed -l gpu=4. Most jobs will only make use of one GPU.

If your job requires selecting the type of GPU hardware, one of the following two optional parameters can be used to make that choice

qsh -l gpu_arch=nvidia-m2070
qsh -l gpu_arch=nvidia-k40m

Interactive sessions provide you with 2 Gigabytes 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

qsh -l gpu_arch=nvidia-m2070 -l rmem=7G
qsh -l gpu_arch=nvidia-k40m -l rmem=13G

The above will give you 1GB more CPU RAM than GPU RAM for each of the respective GPU architectures.

Submitting batch GPU jobs

To run batch jobs on gpu nodes, edit your jobfile to include a request for GPUs, e.g. for a single GPU

#$ -l gpu=1

You can also use the the gpu_arch discussed aboved to target a specific GPU model

#$ -l gpu_arch=nvidia-m2070

Iceberg GPU Resources


Iceberg currently contains publicly accessible 16 GPU units:

  • 8 Nvidia Tesla Kepler K40M GPU units. Each unit contains 2880 CUDA cores, 12GB of memory and is capable of up to 1.43 Teraflops of double precision compute power.
  • 8 Nvidia Tesla Fermi M2070 GPU units. Each unit contains 448 CUDA cores, 6GB of memory and is capable of up to 515 Gigaflops of double precision compute power.

GPU-enabled Software