This documentation is so far only useful for betatesters. In this group we have
- Gregor von Laszewski
Dear GPU beta testers,
Thank you for signing up as beta testers for the new GPU POD on Rivanna. We appreciate your patience during the longer-than-expected installation phase. This email will unveil some details about the new hardware and provide instructions on access and usage.
Introducing the NVIDIA DGX BasePOD
You might have seen/heard the term SuperPOD in our earlier communications or from other sources. Since then the vendor has rebranded the specific type purchased by UVA as BasePOD, which as of today is comprised of 10 DGX A100 nodes 8 A100 GPU devices and 2 TB local node memory (per node) 80 GB GPU memory (per GPU device) I’ll just refer to it as the POD for the remainder of the email.
Unbeknown to most users, these nodes have been up and running on Rivanna since last summer as regular GPU nodes. We are pleased to inform you that the following Advanced Features have now been enabled on the POD:
- NVLink for fast multi-GPU communication
- GPUDirect RDMA Peer Memory for fast multi-node multi-GPU communication
- GPUDirect Storage with 200 TB IBM ESS3200 (NVMe) SpectrumScale storage array
What this means to you is that the POD is ideal for the following scenarios:
- The job needs multiple GPUs and/or even multiple nodes.
- The job (can be single- or multi-GPU) is I/O intensive.
- The job (can be single- or multi-GPU) requires more than 40 GB GPU memory. (We have 12 A100 nodes in total, 10 of which are the POD and 2 are regular with 40 GB GPU memory per device.)
Detailed specs can be found in the official document (Chapter 3.1):
Accessing the POD
As a token of appreciation, we have created a superpodtest allocation such that you may run benchmarks and tests without spending your own allocation. A single job can request up to 4 nodes with 32 GPUs. Before running multi-node jobs, please make sure it can scale well to 8 GPUs on a single node.
We kindly ask you to keep other beta testers and the general users in mind by refraining from dominating the queue with high-throughput jobs through this allocation.
If you are the PI and wish to delegate the testing work to someone else in your group, you are welcome to provide one or two names with their computing IDs.
Slurm script Please include the following lines:
#SBATCH -p gpu #SBATCH --gres=gpu:a100:X # replace X with the number of GPUs per node #SBATCH -C gpupod #SBATCH -A superpodtest
In Optional: Slurm Option write:
Many of you may have already used the POD by simply requesting an A100 node, since 10 out of the total 12 A100 nodes are POD nodes. Hence, if you do not see any performance improvement, do not be disappointed. As we expand our infrastructure, there could be changes to the Slurm directives and job resource limitations in the future. Please keep an eye out for our announcements.
We will be migrating toward NVIDIA’s NGC containers for deep learning frameworks such as PyTorch and TensorFlow, as they have been heavily optimized to achieve excellent multi-GPU performance. These containers have not yet been installed as modules but can be accessed under /share/resources/containers/singularity:
(NGC has their own versioning scheme. The PyTorch and TensorFlow versions are 2.0.0, 1.15.5, 2.11.0, respectively.)
The singularity command is of the form:
singularity run --nv /path/to/sif python /path/to/python/script
Warning: Distributed training is not automatic! Your code must be parallelizable. If you are not familiar with this concept, please visit:
- TF distributed training https://www.tensorflow.org/guide/distributed_training
- PyTorch DDP https://pytorch.org/docs/stable/notes/ddp.html
Please check the manual for your code regarding the relationship between the number of MPI ranks and the number of GPUs. For computational chemistry codes (e.g. VASP, QuantumEspresso, LAMMPS) the two are oftentimes equal, e.g.
#SBATCH --gres=gpu:a100:8 #SBATCH --ntasks-per-node=8
If you are building your own code, please load the modules nvhpc and cuda which provide NVIDIA compilers and CUDA libraries. The compute capability of the POD A100 is 8.0.
For documentation and demos, refer to the Resources section at the bottom of this page: https://developer.nvidia.com/hpc-sdk
We will be updating our website documentation gradually in the near future as we iron out some operational specifics. GPU-enabled modules are now marked with a (g) in the module avail command as shown below:
TODO: output from maodule avail to be included