EuroCC@Greece announces the 16th Course of HPC Training Series with the subject "Compute at Scale: Concepts and Spotlight on AI, IoT, Simulations".
Date: September 29th, 2025, at 10:00 EET
Location: Online via Zoom
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.
Course Description: This course provides an introduction to essential parallelism concepts that underpin modern HPC and reviews 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 is 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 concludes 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:
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Understand the concepts of parallelism, distribution, scale
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Understand how appear or apply in HPC
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Understand the benefits of exploiting parallelism in HPC
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Recognize how parallelism underpins key domains such as AI, IoT, scientific computing.
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Become familiar with Pytorch DDP
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Become familiar with YOLOv8 computer vision model
Learning Outcome:
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Be able to distinguish between parallelism, distribution, scale
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Compare expected benefits (e.g., speedup, scalability) from different HPC frameworks.
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Familiarize with end-to-end data processing and analysis pipelines in IoT and their practical issues.
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Run and interpret a basic PyTorch DDP training workflow.
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Apply YOLOv8 in a parallel setting for basic computer vision tasks.
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Demonstrate informed judgment as end-users when selecting HPC tools and environments.
Use of interactive methods:
The course contains lectures dedicated to practical demonstrations of popular AI-related tools (the Pytorch DDP module, the YOLO v8 computer vision model), which will also provide code snippets to the audience. Furthermore, the course includes a dedicated Q&A session in order to foster interaction between trainers and trainees.
Prerequisites: A prior knowledge of the basics of the Python language is beneficial for some parts of the course.
Note: Please enter your institutional/corporate email when registering.
