GPUs
Description
This page contains all information you need to successfully submit GPU-jobs on UBELIX.
Important Information on GPU Usage
Code that runs on the CPU will not auto-magically make use of GPUs by simply submitting a job to the ‘gpu’ partition! You have to explicitly adapt your code to run on the GPU, e.g. an CUDA or OpenACC implementation. Also, code that runs on a GPU will not necessarily run faster than it runs on the CPU. For example, GPUs require a huge amount of highly parallelizable tasks. In other words, you must understand the characteristics of your job, and make sure that you only submit jobs to the ‘gpu’ partition that can actually benefit from GPUs.
When submitting to the GPU partition the GPU type specification is required.
GPU Types
UBELIX currently features four types of GPUs. You have to choose an
architecture and use one of the following --gres option to select it.
| Type | SLURM gres option |
|---|---|
| Nvidia Geforce GTX 1080 Ti | --gres=gpu:gtx1080ti:<number_of_gpus> |
| Nvidia Geforce RTX 2080 Ti | --gres=gpu:rtx2080ti:<number_of_gpus> |
| Nvidia Geforce RTX 3090 | --gres=gpu:rtx3090:<number_of_gpus> |
| Nvidia Tesla P100 | --gres=gpu:teslap100:<number_of_gpus> |
Job Submission
Use the following options to submit a job to the gpu partition using the
default job QoS:
#SBATCH --partition=gpu
#SBATCH --gres=gpu:<type>:<number_of_gpus>
QoS job_gpu_preempt
For investors we provide the gpu-invest investor partitions with a specific
QoS per investor that guarantees instant access to the purchased resources.
Nevertheless, to efficiently use all resources, the QoS job_gpu_preempt exists
in the gpu partition. Jobs, submitted with this QoS have access to all GPU
resources, but may be interrupted if resources are required for investor jobs.
Short jobs, and jobs that make use of checkpointing will benefit from these
additional resources.
Example: Requesting any four RTX2080Ti from the resource pool in the gpu
partition:
#SBATCH --partition=gpu
#SBATCH --qos=job_gpu_preempt
#SBATCH --gres=gpu:rtx2080ti:4
## Use the following option to ensure that the job, if preempted,
## won't be re-queued but canceled instead:
#SBATCH --no-requeue
Application Adaptation
Applications do only run on GPUs if they are built specifically to run on GPUs that means with GPU support. There are multiple ways to implement algorithms for GPU usage. The most common ones are low level languages like CUDA or pragma oriented implementations like OpenACC.
CUDA
We provide compiler and library to build CUDA-based application. These are
accessible using environment modules. Use module avail to see which versions
are available:
module avail CUDA
---- /software.el7/modulefiles/all ----
CUDA/8.0.61 cuDNN/7.1.4-CUDA-9.2.88
CUDA/9.0.176 cuDNN/7.6.0.64-gcccuda-2019a (D)
CUDA/9.1.85 fosscuda/2019a
CUDA/9.2.88 fosscuda/2019b (D)
CUDA/10.1.105-GCC-8.2.0-2.31.1 gcccuda/2019a
CUDA/10.1.243 (D) gcccuda/2019b (D)
cuDNN/6.0-CUDA-8.0.61 OpenMPI/3.1.3-gcccuda-2019a
cuDNN/7.0.5-CUDA-9.0.176 OpenMPI/3.1.4-gcccuda-2019b
cuDNN/7.0.5-CUDA-9.1.85
Run module load <module> to load a specific version of CUDA:
module load cuDNN/7.1.4-CUDA-9.2.88
If you need cuDNN you must only load the cuDNN module. The appropriate CUDA version is then loaded automatically as a dependency.
GPU Usage Monitoring
To verify the usage of one or multiple GPUs the nvidia-smi tool can be
utilized. The tool needs to be launched on the related node. After the job
started running, a new job step can be created using srun and call
nvidia-smi to display the resource utilization. Here we attach the process to
an job with the jobID 123456. You need to replace the jobId with your gathered
jobID previously presented in the sbatch output.
$ sbatch job.sh
Submitted batch job 123456
$ squeue --me
# verify that job gets started
$ srun --ntasks-per-node=1 --jobid 123456 nvidia-smi
Fri Nov 11 11:11:11 2021
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 495.29.05 Driver Version: 495.29.05 CUDA Version: 11.5 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 NVIDIA GeForce ... On | 00000000:04:00.0 Off | N/A |
| 23% 25C P8 8W / 250W | 1MiB / 11178MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
| 1 NVIDIA GeForce ... On | 00000000:08:00.0 Off | N/A |
| 23% 24C P8 8W / 250W | 1MiB / 11178MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
Therewith the GPU core utilization and memory usage can be displayed for all GPU cards belonging to that job.
Note that this is a one-off presentation of the usage and the called
nvidia-smi command runs within your allocation. The required resources for
this job step should be minimal and should not noticeably influence your job
performance.
Further Information
CUDA: https://developer.nvidia.com/cuda-zone
CUDA C/C++ Basics: http://www.nvidia.com/docs/IO/116711/sc11-cuda-c-basics.pdf
Nvidia Geforce GTX 1080 Ti: https://www.nvidia.com/en-us/geforce/products/10series/geforce-gtx-1080-ti
Nvidia Geforce RTX 2080 Ti: https://www.nvidia.com/de-de/geforce/graphics-cards/rtx-2080-ti/
Nvidia Geforce RTX 3090: https://www.nvidia.com/de-de/geforce/graphics-cards/30-series/rtx-3090/
Nvidia Tesla P100: http://www.nvidia.com/object/tesla-p100.html