EuroCC@Greece announced the 16th Course of HPC Training Series with the subject “Compute at Scale: Concepts and Spotlight on AI, IoT, Simulations”, that took place online on  September 29th, 2025.

Presentation language: Greek

Audience: Suitable for students, researchers, and engineers interested in gaining an understanding of essential concepts of parallelism and a practical grasp of its role in data-intensive domains of HPC.

Location: Online via Zoom

This course provided an introduction to essential parallelism concepts that underpin modern HPC and reviewed how these principles underlie some of the most popular real-world use cases of high-performance computing (HPC) in the domains of AI, streaming systems, and scientific computing. The goal of the course was to help the participants take a step towards becoming well-informed users of technologies and frameworks that exploit parallelism, and to be introduced to the aspects they should consider when  gauging which set-ups may be suitable for their needs, as well as what benefits they can expect. To bridge theory with practice, the webinar concluded with two demo sessions: an introductory tutorial on PyTorch with Distributed Data Parallel (DDP), and a demo of the YOLOv8 model for scalable computer vision.

Learning Objectives:

  • Understand the concepts of parallelism, distribution, scale
  • Understand how appear or apply in HPC
  • Understand the benefits of exploiting parallelism in HPC
  • Recognize how parallelism underpins key domains such as AI, IoT, scientific computing.
  • Become familiar with Pytorch DDP
  • Become familiar with YOLOv8 computer vision model

Learning Outcome:

  • Be able to distinguish between parallelism, distribution, scale
  • Compare expected benefits (e.g., speedup, scalability) from different HPC frameworks.
  • Familiarize with end-to-end data processing and analysis pipelines in IoT and their practical issues.
  • Run and interpret a basic PyTorch DDP training workflow.
  • Apply YOLOv8 in a parallel setting for basic computer vision tasks.
  • Demonstrate informed judgment as end-users when selecting HPC tools and environments.

Use of interactive methods:

The course contained lectures dedicated to practical demonstrations of popular AI-related tools (the Pytorch DDP module, the YOLO v8 computer vision model), which also provided code snippets to the audience. Furthermore, the course included a dedicated Q&A session in order to foster interaction between trainers and trainees.

Presentation Material of the Course can be found here.

Watch the Course’s recordings in the dedicated playlist here.

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