Difference between revisions of "GPU Access"

Latest revision as of 18:02, 27 June 2024

GPUs Per Group

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.

In order to use GPU resources HPG groups need to have an active NGU investment. To check if your group(s) has GPUs allocated and available with the command $ slurmInfo -g group_name (with the module "ufrc" loaded).

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

Select a GPU Partition

There are two partitions that contain GPUs: the hwgui partition for visualization and the gpu partition for general GPU computation.

Open On Demand 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

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 GPU partitions are listed below.

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

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

@@ Line 3: / Line 3: @@
    |__TOC__
    |}
-{{Note|Interactive Jobs in the GPU partition are limited to 12 hrs|warn}}
+==GPUs Per Group==
 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.
+In order to use GPU resources HPG groups need to have an active NGU investment. To check if your group(s) has GPUs allocated and available with the command <code>$ slurmInfo -g group_name</code> (with the [https://help.rc.ufl.edu/doc/UFRC_environment_module module "ufrc"] loaded).
 Researchers can add NGUs to their allocations by filling out the [https://www.rc.ufl.edu/get-started/purchase-allocation/ Purchase Form] or requesting a [https://www.rc.ufl.edu/services/request-trial-allocation/ Trial Allocation].
-=GPU-enabled Services=
+==Select a GPU Partition==
+There are two partitions that contain GPUs: the ''hwgui'' partition for visualization and the ''gpu'' partition for general GPU computation.
+==Open On Demand Access==
+{{Note|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|warn}}
+To access GPUs using [https://help.rc.ufl.edu/doc/Open_OnDemand 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:
+<div style="column-count:2">
+*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
-Types of GPUs are listed below. Two partitions contain GPUs - the hwgui partition for visualization and the gpu partition for general computation.
+*To request a specific type of GPU, use this gres string (requesting geforce GPUs in this example):
+ gpu:geforce:1
-== Hardware Accelerated GUI ==
+*To request a A100 GPU, use this gres string:
+ gpu:a100:1
+</div>
+==GPU-enabled Services==
+Types of GPU partitions are listed below.
+=== 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 ==
+=== 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 (<code>--partition=gpu</code>).
-=== Hardware Specifications for the GPU Partition===
+==== Hardware Specifications for the GPU Partition====
 We have the following types of NVIDIA GPU nodes available in the "gpu" partition:
-* NVIDIA GeForce GTX 1080Ti, with 2 GPUs per node. See [https://www.geforce.com/hardware/desktop-gpus/geforce-gtx-1080-ti/specifications technical specifications] for reference.
-* NVIDIA GeForce RTX 2080Ti, with 8 GPUs per node. See [https://www.nvidia.com/en-us/geforce/graphics-cards/rtx-2080-ti technical specifications] for reference.
-* NVIDIA Quadro RTX 6000, with 8 GPUs per node. These GPUs have SLI bridging See [https://www.nvidia.com/en-us/design-visualization/quadro/rtx-6000/ technical specifications] for reference.
-* AI NVIDIA DGX A100 SuperPod, with 8 GPUs per node. These GPUs have [https://www.nvidia.com/en-us/data-center/nvlink/ NVSWITCH] interconnects See [https://www.nvidia.com/en-us/data-center/a100/ technical specifications] for reference.
 {| style="margin-left: 5px; width:80%"
 |
 {| class="wikitable" style="text-align: center"
-!GPU!!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
+!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
 |-
-| style="width: 14%;"|GeForce 1080Ti||1||Intel Haswell||128 GB||FDR IB||28||14||2||14||11GB||n/a||geforce
+| style="width: 14%;"|GeForce 1080Ti||1||Intel Haswell||128 GB||FDR IB||28||14||2||14||11GB||n/a||geforce||[https://www.geforce.com/hardware/desktop-gpus/geforce-gtx-1080-ti/specifications Specifications]
 |-
-| style="width: 14%;"|GeForce 2080Ti||32||Intel Skylake||187 GB||EDR IB||32||16||8||4||11GB||2080ti||geforce
+| style="width: 14%;"|GeForce 2080Ti||32||Intel Skylake||187 GB||EDR IB||32||16||8||4||11GB||2080ti||geforce||[https://www.nvidia.com/en-us/geforce/graphics-cards/rtx-2080-ti Specifications]
 |-
-| style="width: 14%;"|GeForce 2080Ti||38||Intel Cascade Lake||187 GB||EDR IB||32||16||8||4||11GB||2080ti||geforce
+| style="width: 14%;"|GeForce 2080Ti||38||Intel Cascade Lake||187 GB||EDR IB||32||16||8||4||11GB||2080ti||geforce||[https://www.nvidia.com/en-us/geforce/graphics-cards/rtx-2080-ti Specifications]
 |-
-| style="width: 14%;"|Quadro RTX 6000||6||Intel Cascade Lake||187 GB||EDR IB||32||16||8||4||23GB||rtx6000||quadro
+| style="width: 14%;"|Quadro RTX 6000 SLI||6||Intel Cascade Lake||187 GB||EDR IB||32||16||8||4||23GB||rtx6000||quadro||[https://www.nvidia.com/en-us/design-visualization/quadro/rtx-6000/ Specifications]
 |-
-| style="width: 14%;"|NVIDIA A100 ||140||AMD EPYC ROME||2 TB||HDR IB||128||16||8||16||80GB||a100||a100
+| style="width: 14%;"|NVIDIA A100 [https://www.nvidia.com/en-us/data-center/nvlink/ NVSWITCH]||140||AMD EPYC ROME||2 TB||HDR IB||128||16||8||16||80GB||a100||a100||[https://www.nvidia.com/en-us/data-center/a100/ Specifications]
 |}
 |}
@@ Line 48: / Line 66: @@
 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). See more examples below.
+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 =
+== 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 =
+== Conda Environments with GPU ==
-== Policy ==
-* After purchase, NGU allocations are included in your groups resources (quality of service).
-* To increase the availability of GPU resources, the time limit for the gpu partition is 7-days (at most <code>#SBATCH --time=7-00:00:00</code>). If you have a workload requiring more time, please create a [https://support.rc.ufl.edu/enter_bug.cgi help request].
-==Interactive Access ==
-In order to request interactive command line access to a GPU under SLURM, use commands similar to these:
-*To request access to one GPU (of any type) for a default 1 hour session:
- srun -p gpu --gpus=1 --pty -u bash -i
-*To request access to two A100 GPUs on a single node for a 3-hour session with 300gb RAM:
- srun -p gpu --nodes=1 --gpus=a100:2 --time=03:00:00 --mem=300gb  --pty -u bash -i
-*To request access to two GeForce GPUs with multiple CPUs:
- srun -p gpu --nodes=1 --gpus=geforce:2 --time=01:00:00 --ntasks=1 --cpus-per-task=8 --mem 300gb  --pty -u bash -i
-Interactive sessions are limited to 12 hours.
-==Open On Demand Access ==
-To access GPUs using Open-On-Demand, 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:3
-*To request multiple GPUs (of any type, use this gres string were n is the number of GPUs you need):
- gpu:3
-*To request a specific type of GPU, use this gres string (requesting geforce GPUs in this example):
- gpu:geforce:3
-*To request a A100 GPU, use this gres string:
+To make sure your code will run on GPUs install a recent <code>cudatoolkit</code> 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.
-  gpu:a100:3
+  mamba install cudatoolkit=11.3 pytorch=1.12.1=gpu_cuda* -c pytorch
-== Batch Jobs ==
+See also [[Conda]].
-For batch jobs, to request GPU resources, use lines similar to the following:
-*In this example, two A100 GPUs on a single server (--nodes defaults to "1") will be allocated to the job:
+== Multiple GPUs ==
-<pre>
+Find the following resource for [https://github.com/YunchaoYang/MultiGPUTraining2023 Multi-GPU Training].
-#SBATCH --partition=gpu
-#SBATCH --gpus=a100:2
-</pre>
-*In this example, two GeForce GPUs on a single server (--nodes defaults to "1") will be allocated to the job:
+== Slurm and GPU Use ==
-<pre>
+View instructions for using GPUs and scheduling GPU jobs with SLURM at [[Slurm and GPU Use]]
-#SBATCH --partition=gpu
-#SBATCH --gpus=geforce:2
-</pre>
-Alternatively, use '<code>--gres=gpu:1</code>' or '<code>--gres=gpu:geforce:1</code>' format. Note, if '--gpus=' format is used SLURM will not provide the data on GPU usage to slurmInfo and those GPUs will not be shown in slurmInfo output.
-If no GPUs are available, your request will be queued and your connection established once the next GPU becomes available. Otherwise, you may cancel your job and try lowering requested resources. If you have requested a longer time than is needed, please be sure to end your session so that the GPU will be available for other users.
-== SLURM Options for A100 GPUs ==
-To use A100 GPUs for interactive sessions or batch jobs, please use one of the following SLURM parameters:
-<pre>
---partition=gpu
---gpus=a100:2
-</pre>
-== Job Script Example ==
-This is a sample script for MPI parallel VASP job requesting and using GPUs under SLURM:
-<div class="mw-collapsible mw-collapsed" style="width:70%; padding: 5px; border: 1px solid gray;">
-''Expand to view a sample script''
-<div class="mw-collapsible-content" style="padding: 5px;">
-<pre>
-#!/bin/bash
-#SBATCH --job-name=vasptest
-#SBATCH --output=vasp.out
-#SBATCH --error=vasp.err
-#SBATCH --mail-type=ALL
-#SBATCH --mail-user=email@ufl.edu
-#SBATCH --nodes=1
-#SBATCH --ntasks=8
-#SBATCH --cpus-per-task=1
-#SBATCH --ntasks-per-node=8
-#SBATCH --ntasks-per-socket=4
-#SBATCH --mem-per-cpu=7000mb
-#SBATCH --distribution=cyclic:cyclic
-#SBATCH --partition=gpu
-#SBATCH --gres=gpu:geforce:4
-#SBATCH --time=00:30:00
-echo "Date      = $(date)"
-echo "host      = $(hostname -s)"
-echo "Directory = $(pwd)"
-module purge
-module load cuda/10.0.130  intel/2018  openmpi/4.0.0 vasp/5.4.4
-T1=$(date +%s)
-srun --mpi=pmix_v3 vasp_gpu
-T2=$(date +%s)
-ELAPSED=$((T2 - T1))
-echo "Elapsed Time = $ELAPSED"
-</pre>
-</div>
-</div>