Introduction to Singularity: Containers for High-Performance Computing on Comet

Presented on Thursday, February 12, 2019, by Marty Kandes, Ph.D.

This 2-hour webinar provides an introduction to running Singularity containers on Comet for users currently using Comet and those who want to know more about running Singularity on XSEDE’s Comet supercomputer. SDSC’s computational scientist Marty Kandes provides an in-depth review of the important issues pertaining to running Singularity in the Comet high-performance ecosystem and includes several useful container examples for you to explore following the webinar.

Related Training Material:  Video Recording  |  Slides 



  • Introduction and Background
  • Brief Overview of Comet
  • Introduction to Singularity
    • Building containers
    • Pulling containers
    • Using existing containers
  • Review of Current Pre-built Containers
  • Important Issues to Consider When Running Singularity on Comet
  • Examples and Demonstration
  • Summary

Slides and a recording will be made available after this webinar.  

Related Training Material:    Comet User Guide  Singularity Tutorial  |  Machine Learning Tutorial (Tensorflow)


About the Instructor:

Marty Kandes, Ph.D., (SDSC | Computational and Data Science Research Specialist)

Marty Kandes is a Computational and Data Science Research Specialist in the High-Performance Computing User Services Group at SDSC. He currently helps manage user support for Comet — SDSC’s largest supercomputer. Marty obtained his Ph.D. in Computational Science in 2015 from the Computational Science Research Center at San Diego State University, where his research focused on studying quantum systems in rotating frames of reference through the use of numerical simulation. He also holds an M.S. in Physics from San Diego State University and B.S. degrees in both Applied Mathematics and Physics from the University of Michigan, Ann Arbor. His current research interests include problems in Bayesian statistics, combinatorial optimization, nonlinear dynamical systems, and numerical partial differential equations.

For more training info see:  Training for Advanced Computing Users