---
title: "Partitions / Storrs HPC Resources"
canonical: "https://kb.uconn.edu/space/SH/26032963610/Partitions%20%2F%20Storrs%20HPC%20Resources"
format: markdown
---
## I. Partitions of the Storrs HPC
Storrs HPC is broken up into **eight partitions**. Each partition refers to a group of [nodes ](https://kb.uconn.edu/space/SH/26033979511/Glossary#Hardware)that have similar hardware (e.g., GPUs), types of usage (e.g., long jobs vs. short high-throughput jobs), and/or levels of priority required to access them. All users have access to the general, general-gpu, debug, lo-core, and hi-core partitions, but access to other [priority nodes](https://kb.uconn.edu/space/SH/26033979511/Glossary#Basics-of-the-Condo-Model) can be [purchased](https://hpc.uconn.edu/storrs/condo-application/).
Storrs HPC also has a wide variety of computational architectures available, each with a different strengths. Some have a lot of [cores](https://kb.uconn.edu/space/SH/26033979511/Glossary#Hardware), others lots of [RAM](https://kb.uconn.edu/space/SH/26033979511/Glossary#Hardware), and others are paired with [GPUs](https://kb.uconn.edu/space/SH/26033979511/Glossary#Hardware). Selecting optimal hardware can increase the efficiency of your research, but the definition of “optimal” will be different depending how you’re using the HPC. Below is a table which summarizes the resources available on the HPC.
### **List of Partitions:**
| | | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| **Name** | **Max Wall Time** | **Nodes** | **Architecture** | **Cores**
**available per node*** | **Total Cores** | **GPUs available per node** | **RAM per Node (GB)** | **Use** |
| general | 12 hours | 41
148 | Epyc64
Epyc128 | 62
126 | 2,542
18,648 | n/a
n/a | 503
503 | General-use, free access.
8 node limit per job |
| general-gpu | 12 hours | 28 | Epyc64 | 62 | 1,736 | 1 or 3 | 503 | General-use, free access.
2 node limit per job. |
| preempt | 12 hours | 7 | Epyc128 | 126 | 882 | n/a | 503 | QoS*-driven; highest priority |
| lo-core | 7 days | 16 | Epyc128 | 126 | 2,268 | n/a | 503 | Long running serial jobs
4 node limit per job. |
| hi-core | 6 hours | 19 | Epyc128 | 126 | 3,528 | n/a | 503 | Highly-parallel jobs
16 node limit per job |
| debug | 30 minutes | 1
11
2 | Epyc64
Epyc128
Epyc64-A100 gpu | 62
126
62 | 62
126
62 | n/a
n/a
1 | 503
503
503 | Job submission testing.
2 node limit per job |
| priority | Default time limit set to 24 hours
Unlimitedwhen time is specified in job submission | QoS* | Epyc64
Epyc128 | 62
126 | QoS* | n/a | 503
503 | All condo/priority CPU jobs |
| priority-gpu | Default time limit set to 24 hours
Unlimitedwhen time is specified in job submission | QoS* | Intel^
Skylake-30^
Skylake-34
Epyc64 | 20^
30^
34
62 | QoS* | 2 or 3^
8^
1 or 3
1, 3, or 4 | 125^
376^
187
503 | All condo/priority GPU jobs |
| class | 4 hours | 1
12 | Skylake-GPU
Epyc128 | 34
126 | 34
1,512 | 1
n/a | 187
503 | For classroom/instructional |
****Available Cores per node – ****2 cores are reserved per node for the OS and storage processes. This is not applied to the Haswell and Broadwell architectures.*
****QOS – ****One can specify a ****Quality of Service (QOS)**** for each job submitted to Slurm. The QoS associated with a job will affect the group’s maximum cumulative core and gpu count (these are known as Group Trackable RESources, or GrpTRES) and job priority. These limits are determined by the number of cores and gpu’s a PI purchases using the Condo model.*
**^GPUs:** denotes nodes with lower-quality, consumer-grade GPUs
***NOTE ****– Total cores depends upon node type/cpu architecture. Epyc 128-core nodes represent the top end of range. OSG Epyc nodes are 64 cores. Priority/preempt node assignments **overlap** with general partition.*
## **II. Node Features**
To make it easier for you to find the right hardware for your research, we have labeled each node with certain **features** like “gpu” (read: has GPUs) or “a100” (read: has A100 GPUs). You can target those features when starting a [job ](https://kb.uconn.edu/space/SH/26033979511/Glossary#HPC-Terminology)on the HPC by using **constraints**. For more info, check out our guide to [job submission](https://kb.uconn.edu/space/SH/26032963685/SLURM+Guide) or [SLURM cheatsheet](https://kb.uconn.edu/space/SH/26449379370/SLURM+Cheatsheet). Below is a list of the features used on the Storrs HPC and their corresponding descriptions.
| **Feature Name** | **Description** |
| --- | --- |
| **cpuonly** | Standard CPU nodes without GPUs; keep gpu nodes free for gpu-intensive job |
| epyc128 | has the AMD EPYC 7713 architecture |
| gpu | Nodes with GPUs |
| a100 | nVIDIA Tesla A100 gpu’s |
| v100 | nVIDIA Tesla v100 gpu’s |
| l40 | nVIDIA Tesla L40 gpu’s (single-precision) |
### Node Descriptions
This describes the general features available on the nodes on the HPC. Please see our [SLURM Cheatsheet](https://kb.uconn.edu/space/SH/26449379370/SLURM+Cheatsheet#Job-Submission) for more in-depth guidance on targeting different architectures and amounts of RAM.
| **Node Architecture** | **Cores Available** | **Memory/RAM Available ** | **Flags for Requesting All of Node’s Memory (if all cores requested)** |
| --- | --- | --- | --- |
| Epyc128
(non-OSG partition) | 126 | 503 GB | `--mem-per-cpu=3G` for 378G per node
`--mem-per-cpu=4090M` for 503G per node
`--mem-per-cpu=4188680K` for 503G per node |
### GPU Specifications
| **GPU Type** | **Memory per Card ** |
| --- | --- |
| nVIDIA Tesla A30 | 24.576 GB |
| nVIDIA Tesla A100 | 40.960 GB |
| nVIDIA Tesla L40 (priority-gpu) | 46.068 GB |
## III. Job Submission Examples
Generic example:
```
#SBATCH --account=[account] \ #Specify non-default account
--partition=[partition] \ #Specify queue type
--constraint=["feature""] \ #Specify node feature
--qos=[qos_name] #Specify non-default QoS
```
Preempt example:
```
#SBATCH --account=ena02002 --partition=preempt \
--constraint="epyc128" --qos=manoslabpreempt
```
General submission to specific node types using defaults:
```
#SBATCH --constraint="epyc128" #general Epyc128 submission
#SBATCH --constraint="a100" #general submission to a100 gpu nodes
```
Priority GPU submission to an L40 node:
```
#SBATCH --account=erm12009 --partition=priority-gpu
--constraint="l40" –-qos=maylab
```