GPU Access

Interactive OnDemand Jobs in the GPU partition are limited to 12 hrs. Computational GPU jobs are limited to 14 days. Each GPU job requires at least one CPU core

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.

Researchers can add NGUs to their allocations by filling out the Purchase Form or requesting a Trial Allocation.

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):

gpu:1

To request multiple GPUs (of any type, use this gres string were n is the number of GPUs you need):

gpu:n

To request a specific type of GPU, use this gres string (requesting geforce GPUs in this example):

gpu:geforce:1

To request a A100 GPU, use this gres string:

gpu:a100:1

GPU-enabled Services

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 (--partition=gpu).

Hardware Specifications for the GPU Partition

We have the following types of NVIDIA GPU nodes available in the "gpu" partition:

GPU Specs	Host Quantity	Host Architecture	Host Memory	Host Interconnect	CPUs per Host	CPUS per Socket	GPUs per Host	CPUs per GPU	Memory per GPU	SLURM Feature	GRES GPU type	Technical Ref
GeForce 1080Ti	1	Intel Haswell	128 GB	FDR IB	28	14	2	14	11GB	n/a	geforce	Specifications
GeForce 2080Ti	32	Intel Skylake	187 GB	EDR IB	32	16	8	4	11GB	2080ti	geforce	Specifications
GeForce 2080Ti	38	Intel Cascade Lake	187 GB	EDR IB	32	16	8	4	11GB	2080ti	geforce	Specifications
Quadro RTX 6000 SLI	6	Intel Cascade Lake	187 GB	EDR IB	32	16	8	4	23GB	rtx6000	quadro	Specifications
NVIDIA A100 NVSWITCH	140	AMD EPYC ROME	2 TB	HDR IB	128	16	8	16	80GB	a100	a100	Specifications

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