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More HPC Workshops

Build Your Skills with HPC Trainings and Workshops

Do you need some training so you can use the Roar supercomputer, formerly known as ICDS-ACI, systems more effectively? Or, maybe you’re just interested in learning more about HPC? Browse the list below, which includes both

  • Training sessions offered by ICDS
  • Other learning opportunities

You’ll find informative HPC workshops on topics like optimizing your code, programming in parallel, using specific software applications, and more. Many of the options listed here are free and/or on-demand.

PEARC 2020

Date: Sunday, July 26–Thursday, July 30

Location: Portland, OR

The Practice and Experience in Advanced Research Computing (PEARC) Conference Series is a community-driven effort built on the successes of the past, with the aim to grow and be more inclusive by involving additional local, regional, national, and international cyberinfrastructure and research computing partners spanning academia, government and industry. The PEARC Conference Series is working to integrate and meet the collective interests of our growing community by providing a forum for discussing challenges, opportunities and solutions among the broad range of participants in the research computing community. The PEARC Conferences are organized by a group of dedicated volunteers from the community and are sponsored by the Association for Computing Machinery (ACM), the world’s largest educational and scientific computing society.

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Workshop on Knowledge Guided Machine Learning (KGML)

Date: Tuesday, August 18–Thursday, August 20

Location: Online

Registration deadline: Aug 11, 2020. Registration is free! First-come-first serve (500 slots).

This workshop is part of a 2-year conceptualization project funded by the NSF's Harnessing the Data Revolution (HDR) program involving researchers from the University of Minnesota, University of Wisconsin, Penn State, Colorado State University, and the University of Virginia. This project aims to develop a framework that uses the unique capability of data science models to automatically learn patterns and models from data, without ignoring the treasure of accumulated scientific knowledge. Specifically, the project builds the foundations of knowledge-guided machine learning (KGML) by exploring several ways of bringing scientific knowledge and machine learning models together using pilot applications from four domains: aquatic ecodynamics, climate and weather, hydrology, and translational biology. For more information, please see the project website.

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ACI Training Series Logo

Intermediate HPC Training

Date: Tuesday, September 29

Time: 11:00 a.m.

Location: Online

Users will dive deeper into working with the ICDS-ACI system, including:

  • Version control
  • Compilation automation
  • Diagnosing bottlenecks
  • Scaling studies
  • Basic optimization techniques
  • I/O management


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Debugging and Optimizing Codes with Arm Forge

Date: Wednesday, October 14

Time: 11:00 a.m.–12:30 p.m.

Location: Online

In this training session organized by the Institute for Computational and Data Sciences (ICDS), engineers from Arm will introduce Arm Forge, an integrated environment for debugging and optimizing parallel codes at any scale. The session will provide hands-on demonstrations of how Arm Forge reduces development time, simplifies debugging, and ease application performance enhancement. This training session will focus primarily on two tools, Arm DDT and Arm MAP, both of which are available on ICDS-ACI. Arm DDT: Using sample codes, Arm engineers will walk through the major capabilities of the debugger to illustrate how DDT can debug applications ranging from a single thread to large scale (MPI debug examples used throughout).

  • How to use sparklines to visualize variable values across processes
  • Using semantic analysis tools to catch bugs before you even run the code
  • Illustrate memory debugging to trap array out of bounds errors and memory leaks
  • Using the array viewer to visualize multi-dimensional variables
  • Using watchpoints to stop execution dependent upon expression values
  • Offline debugging for large scale debugging, catching non-deterministic errors and continuous integration
  • Trace points, a flexible and deterministic printf alternative
  • Debugging on a GPU
  • Debugging Python
Arm MAP: The presenters will illustrate how, in a matter of minutes, you can understand the nature of your application’s performance through visualization
  • Isolate workload imbalance issues in codes at any scale
  • See how the amount of time spent in memory operations varies over time and processes
  • Determine how well your application is vectorized
  • Characterize file IO behavior
  • Review profiling capabilities with Python
Attendee Prerequisites: Programming experience in C, C++, Fortran, or Python

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