Ursa User Guide

Ursa System Overview

Ursa is located at the NOAA Environmental Security Computing Center (NESCC), in Fairmont, West Virginia.

Getting Started with Ursa

Log into your NOAA Google account, and you can view a slide presentation introducing Ursa. Log into your NOAA Google account, and you can also review a recorded version of the presentation.

Ursa System Configuration

	Compute System	GPU System
CPU Type	AMD Genoa 9654: 96 cores/12 channels to memory	AMD Genoa 9654: 96 cores/12 channels to memory
CPU Speed (GHz)	2.4 GHz	2.4 GHz
Compute Nodes	576	58
Cores/Node	192	192
Total Cores	110,592	11,136
Memory/Core (GB)	2	2
OS	Rocky 9	Rocky 9
CPU Peak Performance	4.25 PFlops	0.43 PFlops
Interconnect	NDR-200 IB	NDR-200 IB
Total Disk Capacity	>100 PB, (shared with Hera)	>100 PB, (shared with Hera)
Total Ave Disk Performance	>1000 GB/s	>1000 GB/s
GPU Type	N/A	NVIDIA H100-NVL
GPU’s/node	N/A	2
Memory/GPU (GB)	N/A	94
Total GPU FLOPS (TFLOPS)	N/A	3.48 PFlops

Ursa Partitions

Partition	QOS Allowed	Billing TRES Factor	Description
u1-compute	batch,windfall, debug, urgent, long	100	General compute resource. Default if no partition is specified.
u1-h100	gpu, gpuwf	100	For jobs that require nodes with the Nvidia H-100 GPUs.
u1-gh	gpuwf	100	For jobs that require nodes with the Nvidia Grace-Hopper processors.
u1-mi300x	gpuwf	100	For jobs that require nodes with the AMD MI300X GPUs.
u1-service	batch, windfall	100	Serial jobs (max 4 cores), with a 24 hr limit. Jobs will be run on service nodes that have external network connectivity. Useful for data transfers or access to external resources like databases. If your workflow requires pushing or pulling data to/from the HSMS(HPSS), it should be run there. This partition is also a good choice for doing your compilations and builds of your applications and libraries rather than doing it on the login nodes.

See the Quality of Service (QOS) table for more information.

Ursa Front Ends and Service Partition

Ursa has 15 outward-facing nodes.

4 nodes will be (front-end) login/cron nodes interactive use:
- ufe01-ufe04, total of 768 cores for interactive use. See the Login (Front End) Node Usage Policy for important information about using Login nodes.
10 nodes will comprise the service partition:
- 3,840 cores total.
- Available via Slurm.
- Target for compilation and data transfer jobs.
- Target for scrontab jobs (Scrontab is preferred for recurring jobs).
1 node is available for ecflow
- uecflow01

Using GPU Resources on Ursa

Ursa has 2 H100 GPUs each with 94GB of memory in the u1-h100 partition as indicated in the table above. This partition is only accessible from the gpu and gpuwf QOSes.

In order to have priority access to the GPU resources you will need to have a GPU specific project allocation. Please contact your PI or Portfolio Manager for getting a GPU specific allocation.

All projects with a CPU project allocation on Ursa have windfall access to the GPU resources, and conversely all users with GPU specific project allocations have windfall access to the non-GPU resources.

Using GPU Resources With a GPU allocation

If you have a GPU specific project allocation on Ursa you can access the GPUs by submitting to the u1-h100 partition and gpu QOS as shown in the example below where 2 H100 GPUs on 1 node are being requested:

sbatch -A mygpu_project -p u1-h100 -q gpu -N 1 –-gres=gpu:h100:2 my_ml.job

Using GPU Resources Without a GPU allocation

Users that do not have GPU specific project allocations on Ursa can access the GPU resources at windfall priority. Which means users will be able to submit jobs to the system, but they will only run when the resources are not being used by projects that do have a GPU specific project allocation. This is helpful for users who are in interested in exploring the GPU resources for their applications. To use the system in this mode please submit the jobs to the u1-h100 partition and gpuwf QOS as shown in the example below where 2 H100 GPUs on 1 node are being requested:

sbatch -A mycpu_project -p u1-h100 -q gpuwf -N 1 –-gres=gpu:h100:2 my_ml.job

Using the Exploratory GPU Resources

In addition to the NVIDIA H100 GPU system (Partition: u1-h100), two new small GPU exploratory systems with the newer GPU types are available for experimentation. These systems are connected to the Ursa IB network and have access to the Ursa file systems. All projects with access to Ursa have equal access to the new Exploratory Systems via the gpuwf QOS.

To access these nodes, login to Ursa and submit an interactive batch job requesting these GPU resources. Once you have an interactive shell, you can compile and run your applications on those nodes. Vendor provided software is available by loading the appropriate modules. Please run the module spider command to see the list of modules available.

Description of the two exploratory systems:

Partition: u1-gh. Eight Grace Hopper nodes with one NVIDIA GH200 Grace Hopper Superchip with NVIDIA software. These nodes have a single NDR200 connection to Ursa IB fabric. More detailed information about the NVIDIA GH200 is available from NVIDIA.
Partition: u1-mi300x. Three dual-Intel CPU sockets with 8 AMD Mi300x APUs nodes with AMD ROCm software. These nodes have a single NDR200 connection to the Ursa IB fabric. Click AMD MI300X for more detailed information.

Run one of the following commands to get interactive access to these nodes:

salloc -A mygpu_project -t 480 -p u1-gh     -q gpuwf -N 1 –-gres=gpu:gh200:1
salloc -A mygpu_project -t 480 -p u1-mi300x -q gpuwf -N 1 –-gres=gpu:mi300x:2

In the examples above, the first example requests one node with one GH200 GPU and the second example requests one node with two MI300X GPUs.

Ursa Software Stack

Ursa OS is Rocky 9.4, similar to MSU systems (Rocky 9.1) whereas Hera/Jet are Rocky 8.
Module layout is more akin to what you see on MSU systems; installed using spack.
- Please run the module spider command to see all the available modules!
Compilers: Intel’s oneapi, Nvidia’s nvhpc, and AMD’s AOCC compilers are available.
MPIs: Intel MPI from Intel, HPC-X MPI from Nvidia, and openMPI implementations are available.
- We have seen much better performance and stability with HPC-X in our testing of communication intensive benchmarks as it is optimized to take advantage of the NDR-200 IB network more effectively.
An Intel stack is in place. Other stacks will be considered if requested.

Ursa File Systems

Ursa will only mount the two new HPFS files systems, /scratch3 and /scratch4.
Hera is now mounting /scratch[3,4] to allow easier data migration and the running of Hera jobs on the new file systems as well as the old file systems.
Scratch file systems are NOT backed up!

Caution

Data migration deadline:: The /scratch[1,2] file systems will be decommissioned in August. Plan to complete your migration to the /scratch[3,4] file systems no later than 7/31/25.

Usage/Quota information for `/scratch[3,4]` file systems

The new file systems /scratch[3,4] on Ursa and Hera have a performance improving feature called “Hot Pools”. With Hot Pools, the first 1 GB of each file is written to the fast SSD (hot) tier, by default. After some time, usually 10 to 15 minutes, the file is mirrored to the slower HDD (cold) tier and will be double counted as usage toward your quota. As long as the file is actively used, it will stay on both tiers (hot and cold). Unused files are removed from the hot tier and reside only on the cold tier.

As a result the reported usage for the first 1 GB of active files may be doubled.

Cron and Scrontab Services

On Ursa both cron and scrontab services are available. We strongly recommend using scrontab instead of cron whenever possible. For information on how to use scrontab please see scrontab.

Getting Help

For any Ursa or Rhea issue, open a help request.