UAB Compute Cluster a.k.a. Cheaha

Overview

Cheaha is a large, multi-unit computational system for running massively parallel compute tasks. It is managed by the UAB Research Computing Group

Cheaha is currently the fastest supercomputer in the state of Alabama with a theoretical throughput of approximately 450 TFlop/s (HUGE COMPUTE!) and consists of over 3000 CPU cores and 72 NVIDIA-P100 GPU’s. Cheaha is supported by a high-speed parallel filesystem (GPFS) that can store 6 PB non-redundantly and 4 PB redundantly (with more to come!) interconnected by a high speed infiniband network. UAB researchers use Cheaha for wide variety of research such as genomics, neuro-imaging, machine learning, statistical genetics, cancer detection etc.

Access (Cluster Account Request)

To get started using Cheaha, simply visit our Open OnDemand portal at https://rc.uab.edu. This is the primary entry point for Cheaha and provides access to all cluster services directly from your web browser, including graphical desktops, Jupyter Notebooks, and even the traditional command-line.

Access Cheaha Open OnDemand Portal

If you don’t already have an account, you will be prompted to create one the first time you log into the portal. If you are creating an account, please share some of your interests in using Cheaha as this help us understand the science interests of our users.

Logging In

Once your account has been created, you’ll receive an email containing your user ID, generally your Blazer ID. You can log into Cheaha via your web browser using the new web-based HPC experience.

Login to Cheaha

You can also log into Cheaha via a traditional SSH client. Most UAB Windows workstations already have an SSH client installed, possibly named SSH Secure Shell Client or PuTTY. Linux and Mac OS X systems should have an SSH client installed by default.

Usage of Cheaha is governed by UAB’s Acceptable Use Policy (AUP) for computer and network resources.

More Login Information

Login Credentials

Field Cheaha Settings
Remote host cheaha.rc.uab.edu
Port 22
username blazerid
password blazerid password

Storage

User Storage

Each user is allocated 5 TB of personal storage by default. This storage quota is shared between the USER_DATA (/data/user/<blazerid>) and the HOME (/home/<blazerid>) directories. More information on storage can be found here.

Project Directories

In addition to personal storage, Primary Investigators may request additional shared storage for their lab personnel. This space is given a default size of 25 TB. Each PI may have one project space. To request project storage space, the PI should email support at support@listserv.uab.edu with the name of the project as well as the Blazer IDs of the researchers to give access to. Any future requests for giving or removing access must come from the PI.

Python on Cheaha

Anaconda is a free and open-source distribution of the Python and R programming languages for scientific computing, that aims to simplify package management and deployment. Package versions are managed by the package management system conda. CDGS plans on using conda on the cluster for multiple projects involving the use of python.

Anaconda Documentation

Conda Shortcuts on Cheaha

  • Enabling Conda Module on Cluster module avail Anaconda3

  • Creating new Conda Environment conda create --name test_env Packages can be included within the new environment with a similar command conda create --name test_env PACKAGE_NAME
  • List available virtual environments available conda env list Virtual environment with the asterisk(*) next to it is the one that’s currently active
  • Activating conda virtual environment source activate test_env
  • Deactivating Virtual Environment source deactivate
  • Export Conda virtual environmnet to share conda env export -n test_env > environment.yml
  • Creating Conda Virtual Environment from environment.yml conda env create -f environment.yml -n test_env
  • Deleting a Conda Virtual Environment conda remove --name test_env --all

For a complete tutorial and for a most up-to-date version, please use the tutorial from UAB Research Compute’s Anaconda documenation.