Using GPUs

NOTE: The GPU resources are primarily owned by the imacm and etechnik groups. All other groups are allowed to submit a limited number of jobs with a lower priority to increase utilization. We may have to block access to GPU resources by other groups in times of high demand. You can check your own group affiliation with sshare -U.

Slurm: Using GPUs

This guide is introducing our newly available GPU nodes. The configuration is still very fresh, so please contact us if there are problems or if you expect something to behave differently.

Hardware

There are 5 identical GPU nodes available, called gpu21[001-005]. Each node has the following specs:

8 GPUs: NVidia HGX A100
128 Cores: Two sockets with AMD EPYC 7763 64-Core Processors each
2TB Memory, resulting in 16GB per core
14TB of disk space at /data/. Please clean up your data when you are done

Certain cores are associated to a certain GPU and can check the affinity between them with nvidia-smi:

$ srun -N1 -p gpu -A <yourGroupAccount>_gpu -n1 --cpus-per-task 128 --gpus 8 --gpus-per-task 8 nvidia-smi topo -m
        GPU0    GPU1    GPU2    GPU3    GPU4    GPU5    GPU6    GPU7    mlx5_0  CPU Affinity    NUMA Affinity
GPU0     X      NV12    NV12    NV12    NV12    NV12    NV12    NV12    SYS     48-63   3
GPU1    NV12     X      NV12    NV12    NV12    NV12    NV12    NV12    SYS     48-63   3
GPU2    NV12    NV12     X      NV12    NV12    NV12    NV12    NV12    PXB     16-31   1
GPU3    NV12    NV12    NV12     X      NV12    NV12    NV12    NV12    PXB     16-31   1
GPU4    NV12    NV12    NV12    NV12     X      NV12    NV12    NV12    SYS     112-127 7
GPU5    NV12    NV12    NV12    NV12    NV12     X      NV12    NV12    SYS     112-127 7
GPU6    NV12    NV12    NV12    NV12    NV12    NV12     X      NV12    SYS     80-95   5
GPU7    NV12    NV12    NV12    NV12    NV12    NV12    NV12     X      SYS     80-95   5
mlx5_0  SYS     SYS     PXB     PXB     SYS     SYS     SYS     SYS      X

Legend:

  X    = Self
  SYS  = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)
  NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node
  PHB  = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)
  PXB  = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)
  PIX  = Connection traversing at most a single PCIe bridge
  NV#  = Connection traversing a bonded set of # NVLinks

Job Submission and Accounting

The GPU nodes are available in their own gpu partition:

$ scontrol show partition gpu
PartitionName=gpu
   AllowGroups=ALL AllowAccounts=astro_gpu,cobra_gpu,fugg_gpu,imacm_gpu,lrz_gpu,modellierung_gpu,ops_gpu,optimierung_gpu,risiko_gpu,stroemung_gpu,whep_gpu,zim_gpu AllowQos=ALL
   AllocNodes=ALL Default=NO QoS=N/A
   DefaultTime=NONE DisableRootJobs=NO ExclusiveUser=NO GraceTime=0 Hidden=NO
   MaxNodes=UNLIMITED MaxTime=3-00:00:00 MinNodes=0 LLN=NO MaxCPUsPerNode=UNLIMITED
   Nodes=gpu21[001-005]
   PriorityJobFactor=1 PriorityTier=1 RootOnly=NO ReqResv=NO OverSubscribe=NO
   OverTimeLimit=NONE PreemptMode=OFF
   State=UP TotalCPUs=640 TotalNodes=5 SelectTypeParameters=NONE
   JobDefaults=(null)
   DefMemPerCPU=16000 MaxMemPerCPU=32000

As you can see in AllowAccounts, we only allow a special purpose Slurm account with a suffix of _gpu to submit jobs to the node. Every user is associated to their groups account and a corresponding _gpu account. This way we can have separate fairshare configurations between CPU resources and GPU resources, which might be necessary to prioritize research groups with a larger demand for GPU resources. You can check the current share distribution with sshare yourself.

Slurm treats GPUs as consumable resources and you must specify how many GPUs you would like to request for your job with --gpus 8. Additionally you should decide how many tasks (e.g. processes, option -n) you plan to run on a given node and how many --gpus-per-task and --cpus-per-task you require. All of these options depend on the scaling behavior of you code and how many CPUs per GPU you need.

To summarize, whenever you intend to submit a job with GPU resources, consider the following options:

-p gpu to submit to the gpu partition
-A <yourGroupAccount>_gpu to use your groups special purpose account for the fairshare evaluation
Number of nodes, e.g. -N1, and number of tasks, e.g. -n1
--gpus <N>, where N is the number of GPUs you require for your job (up to 8 per node)
--gpus-per-task <N> to set the number of GPUs to be used in a single process/task (up to 8)
--cpus-per-task <N> to set the number of CPU cores in a single task (up to 128 per node)

Here is an example job script, submitting 8 processes, each with 16 cores and one GPU to a single node:

#!/bin/sh
#SBATCH --job-name=gputests
#SBATCH --partition=gpu
#SBATCH --account=<groupname>_gpu
#SBATCH -N 1
#SBATCH --ntasks 8
#SBATCH --cpus-per-task 16
#SBATCH --gpus-per-task 1
#SBATCH --time=0-01:00:00
#SBATCH -o %x-%j.out

srun -n8 nvidia-smi topo -m

Software

Some basic packages are installed on all GPU nodes:

# yum list installed | grep nvidia
Loaded plugins: fastestmirror, nvidia
cuda-drivers.x86_64                    495.29.05-1                     @local-centos7.9-x86_64--install-repos-nvidia
kmod-nvidia-latest-dkms.x86_64         3:495.29.05-1.el7               @local-centos7.9-x86_64--install-repos-nvidia
nvidia-driver-latest-dkms.x86_64       3:495.29.05-1.el7               @local-centos7.9-x86_64--install-repos-nvidia
nvidia-driver-latest-dkms-NVML.x86_64  3:495.29.05-1.el7               @local-centos7.9-x86_64--install-repos-nvidia
nvidia-driver-latest-dkms-NvFBCOpenGL.x86_64
                                       3:495.29.05-1.el7               @local-centos7.9-x86_64--install-repos-nvidia
nvidia-driver-latest-dkms-cuda.x86_64  3:495.29.05-1.el7               @local-centos7.9-x86_64--install-repos-nvidia
nvidia-driver-latest-dkms-cuda-libs.x86_64
                                       3:495.29.05-1.el7               @local-centos7.9-x86_64--install-repos-nvidia
nvidia-driver-latest-dkms-devel.x86_64 3:495.29.05-1.el7               @local-centos7.9-x86_64--install-repos-nvidia
nvidia-driver-latest-dkms-libs.x86_64  3:495.29.05-1.el7               @local-centos7.9-x86_64--install-repos-nvidia
nvidia-fabric-manager.x86_64           495.29.05-1                     @local-centos7.9-x86_64--install-repos-nvidia
nvidia-libXNVCtrl.x86_64               3:495.29.05-1.el7               @local-centos7.9-x86_64--install-repos-nvidia
nvidia-libXNVCtrl-devel.x86_64         3:495.29.05-1.el7               @local-centos7.9-x86_64--install-repos-nvidia
nvidia-modprobe-latest-dkms.x86_64     3:495.29.05-1.el7               @local-centos7.9-x86_64--install-repos-nvidia
nvidia-persistenced-latest-dkms.x86_64 3:495.29.05-1.el7               @local-centos7.9-x86_64--install-repos-nvidia
nvidia-settings.x86_64                 3:495.29.05-1.el7               @local-centos7.9-x86_64--install-repos-nvidia
nvidia-xconfig-latest-dkms.x86_64      3:495.29.05-1.el7               @local-centos7.9-x86_64--install-repos-nvidia
yum-plugin-nvidia.noarch               0.5-1.el7                       @local-centos7.9-x86_64--install-repos-nvidia

Additionally, you can try some of the available modules with GPU related features:

CUDA/11.4.2
NVHPC/21.7: The successor of the PGI compilers
Various debuggers and profilers, e.g. TotalView/2021.3.9 and ARMForge/21.1.1, as well as Intel parallel studio XE
TensorFlow/2.6.0

In all of these cases you would run module load 2021a <module/version> in your sbatch job script or your interactive salloc/srun allocation to gain access to the corresponding tools. The modules have not been tested in this context yet, so please get in touch if something does not work as expected.