Normalized Graphics Processor Units (NGUs) include all of the infrastructure (memory, network, rack space, cooling) necessary for GPU-accelerated computation. Each NGU is equivalent to 1 GPU presently, however newer GPUs such as the A100s may require more than 1 NGU to access in the future.
Open On Demand Access
To access GPUs using Open OnDemand, please check the form for your application. If your application supports multiple GPU types, choose the GPU partition and specify number of GPUs and type:
- To request access to one GPU (of any type, use this gres string):
- To request multiple GPUs (of any type, use this gres string were n is the number of GPUs you need):
- To request a specific type of GPU, use this gres string (requesting geforce GPUs in this example):
- To request a A100 GPU, use this gres string:
Types of GPUs are listed below. Two partitions contain GPUs - the hwgui partition for visualization and the gpu partition for general computation.
Hardware Accelerated GUI
GPUs in these servers are used to accelerate rendering for graphical applications. These servers are in the SLURM "hwgui" partition. Refer to the Hardware Accelerated GUI Sessions page for more information on available resources and usage.
GPU Assisted Computation
A number of high performance applications installed on HiPerGator implement GPU-accelerated computing functions via CUDA to achieve significant speed-up over CPU calculations. These servers are in the SLURM "gpu" partition (
Hardware Specifications for the GPU Partition
We have the following types of NVIDIA GPU nodes available in the "gpu" partition:
For a list of additional node features, see the Available Node Features page.
To select a specific type of GPU within a partition please use either a SLURM constraint (e.g. --constraint=rtx6000) or a GRES with the needed GPU type (--gres or --gpu=a100:1).
Compiling CUDA Enabled Programs
The most direct way to develop a custom GPU accelerated algorithm is with the CUDA programming, please refer to the Nvidia CUDA Toolkit page. The current CUDA environment is cuda/11. However, C++ or Python packages numba and PyCuda are other ways to program GPU algorithms.
Slurm and GPU Use
View instructions for using GPUs and scheduling GPU jobs with SLURM at Slurm and GPU Use