CUIT High Performance Computing (HPC)
Introduction
Welcome to the home page for Columbia University's High-Performance Computing (HPC) resources!
The CUIT High-Performance Computing service provides powerful cluster resources that support data-intensive and computationally demanding research across numerous departments and groups at the University. The clusters are administered and supported by the CUIT HPC team and governed by the faculty-led Shared Research Computing Policy Advisory Committee (SRCPAC).
NOTE: Sensitive data is not allowed on any of our clusters. Please reference the University Data Classification Policy.
Getting Access
Access to the cluster is subject to formal approval by selected members of the participating research groups. See the HPC CUIT Webpage under “Make a Request” for more information on access options.
Cluster User Documentation
Workshop & Training
Spring 2026 Training Series
Register for our revamped training series to get the most out of your time on the cluster:
Thursday, April 9 | HPC Training #1: Introduction to High-Performance Computing (HPC)
Tuesday, April 14 | HPC Training #2: Editing Tools within HPC (Vim)
Thursday, April 16 | HPC Training #3: Navigating an HPC Environment with Linux
Tuesday, April 21 | HPC Training #4: Scheduling Your First HPC Jobs with Bash
Thursday, April 23 | HPC Training #5: Optimizing HPC Workloads with Python
Tuesday, April 28 | HPC Training #6: Harnessing Accelerated HPC Workloads with Singularity
Training Series Recordings
If you're not familiar with basic Linux commands and usage, or if you need a refresher on these topics, please refer to the following resources from our workshop series:
Intro to Linux: and workshop recording
Intro to Bash Shell Scripting: and workshop recording
Intro to Python for HPC: and workshop recording
Intro to High Performance Computing: and workshop recording
Other resource links
RCS Video Library with past training recordings
Research Computing (RC) Training & Workshop page with calendar of upcoming events across Columbia
Foundations for Research Computing mailing list to monthly email recap of upcoming research computing workshops at Columbia
Featured Links
Getting Help
Interactive Job Policy
Maintenance Schedule
New Queue System
Available Resources
Insomnia went live in February 2024 and initially was a joint purchase by 21 research groups and departments. Unlike its predecessors, this new high performance computing design allows researchers to buy not only a node but half or even a quarter of a node on the cluster.
Insomnia is faculty-governed by the cross-disciplinary SRCPAC and is administered and supported by CUIT’s High Performance Computing team.
Insomnia is a new type of design intended to allow indefinite expansion to Columbia's shared high performance computing cluster, adding new hardware and capabilities as needed. It is a perpetual cluster. Hardware will be retired after five (5) years.
All of Insomnia's high performance computing servers are equipped with Dual Intel Xeon Platinum 8460Y processors (2 GHz):
Total nodes: 90
Standard nodes: 41
High-memory nodes: 19
Total GPU nodes: 30
A600 x 8 GPU nodes: 13
A600 x 4 GPU nodes: 2
H100 x 2 GPU nodes: 3
L40 x 2 GPU nodes: 3
L40s x 2 GPU nodes: 9
Total storage:
2.282 PB GPFS filesystem
CPU resources:
Physical cores: 7,144
Logical CPUs (including hyperthreads): 14,288
Total memory:
~58.3 TiB
HDR Infiniband
Red Hat Enterprise Linux 9.3
Slurm job scheduler
See more information here.
Ginsburg high performance computing went live in February 2021 and is a joint purchase by 33 research groups and departments.
The cluster is faculty-governed by the cross-disciplinary SRCPAC and is administered and supported by CUIT’s High Performance Computing team.
Tentative retirement dates
Ginsburg Phase 1 retirement: February 2026
Ginsburg Phase 2 retirement: March 2027
Ginsburg Phase 3 retirement: December 2027
All of Ginsburg's high performance computing servers are equipped with Dual Intel Xeon Gold 6226R processors (2.9 GHz):
Total nodes: 286
Standard nodes: 191
High-memory nodes: 56
Total GPU nodes: 39
RTX 8000 x 2 GPU nodes: 18
V100S x 2 GPU nodes: 4
A100 x 2 GPU nodes: 8
A40 x 2 GPU nodes: 9
Total storage:
1PB of DDN ES7790 Lustre storage
CPU resources:
Physical cores: 4,576
Logical CPUs (including hyperthreads): 9,152
Total memory:
~ 77.10 TB
HDR Infiniband
Red Hat Enterprise Linux 8
Slurm job scheduler
See more information here.