This two-day hybrid course serves as an introduction to the LUMI architecture and setup. It will include lessons about the hardware architecture, compiling, using software and running jobs efficiently. After the course, you will be able to work effectively on both the CPU (LUMI-C) and the GPU partition (LUMI-G).


Location: TalTech University, Tallinn, Estonia
Format: 2-day hybrid course (online and onsite)
Cost: Free
Registration deadline: October 14, 16:00 CEST / 17:00 EEST

Requirements: Participants must join the course-specific training project and set up a LUMI account (instructions provided after registration)

Prerequisites

Basic Unix shell knowledge and HPC cluster computing experience
Familiarity with C, Fortran, or Python (recommended)
Previous general HPC introduction course or equivalent experience.
General HPC refresher material - https://carpentries-incubator.github.io/hpc-intro/
Basic usage of LUMI - https://docs.lumi-supercomputer.eu/firststeps/

Target Audience

Current and future LUMI supercomputer users
Support staff from LUMI consortium member organizations

What You'll Learn

Connect to LUMI and transfer data from and to the cluster
Understand LUMI hardware and compile software effectively
Use module system and EasyBuild for software management
Submit and manage Slurm jobs (including job arrays and GPU/CPU binding)
Identify and mitigate I/O bottlenecks in the LUSTRE file system
Create Python environments and run containers

Course Details

Instructors: LUMI User Support Team (LUST), HPE & AMD experts
Materials: https://lumi-supercomputer.github.io/LUMI-training-materials/
Help: https://lumi-supercomputer.eu/user-support/need-help/

Important:

This is an introduction to the specifics of LUMI and not a general HPC intro course. AI users are recommended to participate in the training "Moving your AI training jobs to LUMI" workshop (Oct 8-9, 2025)
Participants arrange their own travel. Waiting list available if oversubscribed.
If your plans change, we kindly ask you to cancel your registration as soon as possible to free the seats for the waiting list.