About GPU Resources in O2
The first 6 GPU nodes are now available on O2, including: 8 Tesla V100, 8 Tesla M40 and 16 Tesla K80 GPUs. To list information about all the nodes with GPU resources you can use the command:
GPU Partition Limits
The following limits are applied to this partition in order to facilitate a fair use of the limited resources:
The amount of GPU resources that can be used by each user at any time in the O2 cluster is measured in terms of GPU hours / user, currently there is an active limit of 160 GPU hours for each user.
For example at any time each user can allocate* at most 1 GPU card for 120 (due partition wall time limit), 2 GPU cards for 80 hours,16 GPU cards for 10 hours or any other combination that does not exceed the total GPU hours limit.
* as resources allow
The total amount of memory, from all running GPU jobs, that each user can get allocated is set to 420GB
The total amount of CPU cores, from all running GPU jobs, that each user can get allocated is set to 34
Those limits will be adjusted as we migrate additional GPU nodes from the older cluster to O2.
How to compile cuda programs
In most cases a cuda library and compiler module must be loaded in order to compile cuda programs. To see which cuda modules are available use the command module spider cuda, then use the command module load to load the desired version. Currently only the latest version of Cuda toolkit (V 9) is available
How to submit a GPU job
To submit a GPU job in O2 you will need to use the partition gpu and must add the flag --gres=gpu:1 to request a GPU resource. The example below shows how to start an interactive bash job requesting 1 CPU core and 1 GPU card:
While this other example shows how to submit a batch job requesting 2 GPU cards and 4 CPU cores:
It is also possible to request a specific type of GPU card by using the --gres flag. For example --gres=gpu:teslaM40:3 can be used to request 3 GPU Tesla M40 cards. Currently three GPU types are available: teslaM40, teslaK80 and teslaV100.