Indiana Jetstream2 CPU

Jetstream2 CPU is a cloud-based computing resource that provides flexible, on-demand virtual machine (VM) environments for research and education. Unlike traditional HPC systems, Jetstream2 allows users to launch and manage their own instances rather than submitting jobs to a shared scheduler.

The CPU resource consists of nodes powered by AMD Milan 7713 processors with 128 cores per node and 512 GB of RAM, connected via high-speed 100 Gbps Ethernet networking.

Jetstream2 is designed for interactive computing, software development, data analysis, and building custom research environments. It is particularly well suited for users who need full control over their software stack or require persistent, always-on infrastructure.

Ask about Jetstream2 CPU

File Transfer

Jetstream2 does not use dedicated data transfer nodes. All transfers occur directly between the user’s local machine and the virtual machine.

Supported Methods Data Transfer Node URL
GLOBUS | RECOMMENDED https://www.globus.org/data-transfer
SCP
SFTP

Storage

File System

Directory Path Quota Purge Backup Notes

External Storage

Storage Filesystems

Jetstream2 does not use shared HPC file systems. Storage is attached to virtual machines.

Instance Storage

  • Local disk tied to the VM
  • Deleted when the instance is deleted
  • Used for temporary data and runtime files

Persistent Volumes

  • Independent block storage attached to VMs
  • Survives instance restarts
  • Can be detached and reattached to different instances
  • Used for long-term datasets

Typical mount path:

/media/volume/<volume-name>

File Shares

  • Shared storage accessible across multiple VMs
  • Implemented using OpenStack Manila
  • Used for collaboration and shared datasets

Typical mount path:

/media/share/<share-name>


Jobs

Jetstream2 does not use a centralized job scheduler like Slurm. Instead, users run workloads directly on virtual machines that they create and manage.

Jobs are executed interactively or through scripts within the VM environment. For example:

python script.py

If batch scheduling is required, users can deploy their own scheduler (e.g., Slurm, HTCondor, or Kubernetes) inside a virtual cluster.

Resources are allocated based on the size and runtime of virtual machine instances rather than queued jobs.

Queue specifications

Name Purpose CPUs GPUs RAM Jobs
30 days
Wait Time
30-day trend
Wall Time
30-day trend
Indiana Jetstream2 CPU AMD Milan 7713 2 Ghz 4 GB per Node