Partitions and Resources

Last updated on 2026-08-27 | Edit this page

Overview

Questions

  • What partitions are available on Sagehen HPC?
  • How do I choose the right partition for my job?
  • What GPU types are available?

Objectives

  • Understand the characteristics and use cases for each Sagehen partition
  • Match job requirements to the appropriate partition
  • Know the available GPU types and their strengths

The “amd” Partition (General Compute)

The workhorse of Sagehen, suitable for most research computing.

For the current node count, CPU count, and RAM configuration, run sinfo -p amd on Sagehen.

Property Value
Configuration See sinfo -p amd for current details
Max job time 720 hours (30 days)

Use cases: Statistical analysis, bioinformatics, physics simulations, data processing, batch processing.

The “gpu” Partition (GPU Acceleration)

For workloads requiring GPU acceleration.

Property Value
Nodes Multiple GPU-equipped nodes
CPUs per node 128 cores, 500 GB RAM
Max job time 720 hours (30 days)

Available GPU Types

Sagehen has 10 GPUs total (confirmed May 2026):

GPU Type Memory Quantity Best For
NVIDIA A100 80 GB HBM2e 4 Large models, production AI
NVIDIA L40S 48 GB GDDR6 4 Deep learning, inference
NVIDIA RTX PRO 6000 96 GB GDDR7 ECC 2 Largest memory on the cluster

Check availability: sinfo -p gpu --Format=NodeList,Gres,GresUsed

Callout

Share GPUs Responsibly

GPU nodes are expensive and limited: - Only request GPUs if your code actually uses them - Use L40S for prototyping, A100 for production - Monitor GPU utilization with nvidia-smi - Request the minimum number you need

The “short” Partition (Quick Jobs and Debugging)

For short-duration test jobs, debugging, and rapid prototyping. The short partition has a shorter maximum walltime than amd and gpu, but jobs typically start more quickly because the scheduler can back-fill them between longer jobs.

Property Value
Configuration See sinfo -p short for current details
Max job time Shorter than amd/gpu — verify with sinfo -p short

Use cases: Quick test of a new submission script, single-task debugging, short interactive sessions, sanity-checking a workflow before launching a 30-day production run.

Example: a quick test job on the short partition

BASH 

#!/bin/bash
#SBATCH --job-name=script_test
#SBATCH --partition=short
#SBATCH --time=00:15:00
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=2
#SBATCH --mem=4G
#SBATCH --output=test_%j.log

module purge
module load miniconda3

python my_script.py --dry-run

If the test succeeds, you can change --partition=short to --partition=amd (and increase --time) for the production run.

A decision tree for choosing a partition. If you need a GPU, use the gpu partition with four A100 80GB, four L40S 48GB and two RTX PRO 6000 96GB cards, allowing up to 720 hours. If not, ask whether the job will finish in under two hours; if it will, use short, which shares the amd nodes and starts almost immediately. Otherwise use amd, the default, with 12 nodes of 128 cores and 500 GB each and up to 720 hours.
Two questions settle it: do you need a GPU, and will the job finish inside two hours?

Choosing the Right Partition

  1. Need GPU acceleration? -> gpu partition
  2. Quick test, debug, or short prototype run (within short walltime limit)? -> short partition
  3. Everything else (production CPU work) -> amd partition

Account and Group Limits

Each account has resource limits to ensure fair sharing:

Limit Details
Max cores per account Per-account limits apply
Max GPUs per account Per-account limits apply
Max submitted jobs Per-account limits apply

Check your group’s quota with quota_check.sh. Contact for current limits or temporary increases.

Challenge

Challenge 1: Matching Jobs to Partitions

For each scenario, decide which partition to use:

  1. Testing a new analysis script (takes about 30 seconds)
  2. Analyzing a 1 TB dataset, expected to take 6 hours
  3. Training a deep learning model, expected to take 18 hours
  4. Running a molecular dynamics simulation for 72 hours
  1. short – Quick test (~30 seconds) is the canonical use case for the short partition
  2. amd – General compute, no GPU needed; 6 hours fits within amd’s 30-day limit
  3. gpu – Deep learning needs GPU acceleration
  4. amd – Long simulation (72 hours), no GPU needed, fits 30-day amd limit
Key Points
  • Sagehen has three partitions: amd (general compute), gpu (accelerators), and short (quick / test / debug jobs)
  • Choose partition based on GPU need and job duration
  • GPU partition offers A100 (production), L40S (inference), and RTX PRO 6000 (large-memory training)
  • Per-account resource limits apply; check with quota_check.sh
  • amd and gpu support up to 720 hours (30 days); short has a shorter walltime — check sinfo -p short