IMPORTANT: The information contained on this page is no longer up-to-date. Please refer to this page on our wiki that shows thew new queue configuration which will be rolled out in April.

GPU class jobs can be requested by selecting from one of the below configurations

GPU typeNumber of gpus nncpus/gpu Xmem/GB YMax walltime/hr
 K80, P100, RTX6000 1-8 4n  24n 24
 P1000  1  1-8  96  72

Where n is a multiplier matching the number of GPUs requested. The additional options ngpus= and gpu_type= must be added to the PBS selection.

For example, to specify a job using 4 RTX6000 GPUs, the selection must be

#PBS -lselect=1:ncpus=16:mem=96gb:ngpus=4:gpu_type=RTX6000

Within the context of the running job, the shell environment variable CUDA_VISIBLE_DEVICES will be set with indices of the allocated GPUs. Jobs must respect this setting, or they will interfere with other jobs co-located on the execution node

The details of the different GPU types are:

GPU type

Single precision /TFLOPS

Double precision/TFLOPS

Memory bandwidth/GB/s
Recommended use
P1000 1.8 <<1 4 80

Interactive use via Jupyter

Ensemble tasks: ML inference, molecular dynamics, image processing, etc

K80 5.6 2.9 12 240 Numerical simulation
P100 8.0 4.0 16 730 Numerical simulation
RTX6000 16.3 <1 24 670 ML training