GPU Access

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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):
  • 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:

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.

Conda Environments with GPU

To make sure your code will run on GPUs install a recent cudatoolkit package that works with the NVIDIA drivers on HPG (currently 12.x, but older versions are still supported) alongside the pytorch or tensorflow package(s). See RC provided tensorflow or pytorch installs for examples if needed. Mamba can detect if there is a gpu in the environment, so the easiest approach is to run the mamba install command in a gpu session. Alternatively, you can run mamba install on any node or if a cpu-only pytorch package was already installed by explicitly requiring a gpu version of pytorch when running mamba install. E.g.

mamba install cudatoolkit=11.3 pytorch=1.12.1=gpu_cuda* -c pytorch

See also Conda.

Multiple GPUs

Find the following resource for Multi-GPU Training.

Slurm and GPU Use

View instructions for using GPUs and scheduling GPU jobs with SLURM at Slurm and GPU Use