Summary and Setup

Outlining the course

  • Targeted audience (see learner profiles: New HPC users, RSE with users on HPC systems, researchers in HPC.NRW)
  • Estimated length and recommended formats (e.g. X full days, X * 2 half days, in-person/online, live-coding)
  • Course intentions (focus on learners perspective!):
    • Speed up research
    • Improve batch utilization through matching application requirements to requested hardware (minimal resource requirements, maximum resource utilization)
    • Convey intuition about job-sizes. What is considered large, what small?
    • Sharpen awareness for importance to avoid wasting time/energy on a shared system
    • Teach common concepts and terms of performance
    • First steps into performance optimizations (cluster-, node-, application level)
  • Well defined context:
    • HPC Systems
    • Performance of jobs
    • Application performance is touched (related to job efficiency), but in-depth is outside of the scope. Next steps point towards deeper performance analyses

Learning Objectives

After attending this training, participants will be able to:

  • Explain efficiency in the context of HPC systems
  • Use batch system tools and third party tools to measure job efficiency
  • Discern between worse and better performing jobs
  • Describe common concepts and terms related to performance on HPC systems
  • Identify hardware components involved in performance considerations
  • Achieve first results in performance optimization of their application
  • Remember next steps to take towards learning performance optimization

Prerequisite

  • Access to a HPC system
  • Example workload setup
  • Basic knowledge of HPC systems (batch systems, parallel file systems) – being able to submit a simple job and understand what happens in broad terms
  • Knowledge of tools to work with HPC systems:
    • Bash shell & scripting
    • ssh & scp

Example Workload & Setup

Example workload that:

  • Has some instructive performance issues that can be discovered, e.g.
    • Mismatch between requested resources in job script and used resources
    • Memory leak or unnecessary allocation with a quick fix? Either triggers OOM or just wasting resources, dependent on side and default memory/core
    • No vectorization?
    • Parallelism issues?
  • Software that can run on CPU and GPU, to discuss both with the example

You will need access to an HPC cluster to run the examples in this lesson. Discuss how to find out where to apply for access as a researcher (in general, in EU, in Germany, in NRW?). Refer to the HPC Introduction lessons to learn how to access and use a compute cluster of that scale.

  • Executive summary of typical HPC workflow? Or refer to other HPCC courses that cover this
  • “HPC etiquette”
    • E.g. don’t run benchmarks on login node
    • Don’t disturb jobs on shared nodes
  • Setup of example for performance studies

Common Software on HPC Systems

Working on an HPC system commonly involves a

  • batch system to schedule jobs (e.g. Slurm, PBS Pro, HTCondor, …), a
  • module system to load certain versions of centrally provided software and a
  • way to log in to a login node of the cluster.

To login via ssh, you can use on (remove this since it’s discussed in HPC introduction?)

  • PuTTY
  • ssh in PowerShell
  • ssh in Terminal.app
  • ssh in Terminal